{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf2faade",
   "metadata": {},
   "source": [
    "# 数据情况概览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "be0f6007",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.168836Z",
     "start_time": "2022-01-12T14:57:02.460837Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "x = pd.read_excel(r'C:\\Users\\hasee\\OneDrive\\桌面\\2021年MathorCup大数据竞赛赛道A\\附件\\附件1：估价训练数据.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4cafae5c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.216464Z",
     "start_time": "2022-01-12T14:57:14.170547Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carid</th>\n",
       "      <th>tradeTime</th>\n",
       "      <th>brand</th>\n",
       "      <th>serial</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>color</th>\n",
       "      <th>cityId</th>\n",
       "      <th>carCode</th>\n",
       "      <th>transferCount</th>\n",
       "      <th>...</th>\n",
       "      <th>af7</th>\n",
       "      <th>af8</th>\n",
       "      <th>af9</th>\n",
       "      <th>af10</th>\n",
       "      <th>af11</th>\n",
       "      <th>af12</th>\n",
       "      <th>af13</th>\n",
       "      <th>af14</th>\n",
       "      <th>af15</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4220*1740*1625</td>\n",
       "      <td>201709.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-06-25</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8.60</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4630*1775*1480</td>\n",
       "      <td>201609.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>15.56</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4515*1725*1445</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.04</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>4500*1834*1707</td>\n",
       "      <td>201608.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4.38</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5.70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-09-20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4315*1783*1606</td>\n",
       "      <td>201204.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   carid  tradeTime  brand  serial  model  mileage  color  cityId  carCode  \\\n",
       "0      1 2021-06-28      1       1      1     4.01      1       1      1.0   \n",
       "1      2 2021-06-25      2       2      2     8.60      1       2      1.0   \n",
       "2      5 2021-06-19      5       5      5    15.56      1       2      3.0   \n",
       "3      6 2021-06-29      6       6      6     6.04      1       3      1.0   \n",
       "4      7 2021-06-30      7       7      7     5.70      4       1      2.0   \n",
       "\n",
       "   transferCount  ...        af7  af8  af9  af10  af11            af12  \\\n",
       "0              0  ...        NaT  1.0  5.0   2.0     1  4220*1740*1625   \n",
       "1              0  ...        NaT  2.0  4.0   3.0   1+2  4630*1775*1480   \n",
       "2              0  ...        NaT  NaN  NaN   NaN   NaN  4515*1725*1445   \n",
       "3              3  ... 2018-08-18  2.0  5.0   2.0     1  4500*1834*1707   \n",
       "4              2  ... 2020-09-20  1.0  5.0   2.0     1  4315*1783*1606   \n",
       "\n",
       "       af13  af14  af15  price  \n",
       "0  201709.0     1   NaT   4.24  \n",
       "1  201609.0     2   NaT   7.38  \n",
       "2       NaN     2   NaT   1.00  \n",
       "3  201608.0     2   NaT   4.38  \n",
       "4  201204.0     1   NaT   5.90  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()  # 预览数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b22eddd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.312169Z",
     "start_time": "2022-01-12T14:57:14.217421Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carid</th>\n",
       "      <th>brand</th>\n",
       "      <th>serial</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>color</th>\n",
       "      <th>cityId</th>\n",
       "      <th>carCode</th>\n",
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       "      <th>price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.000000</td>\n",
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       "      <td>30000.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37099.253567</td>\n",
       "      <td>19.228300</td>\n",
       "      <td>237.707733</td>\n",
       "      <td>5312.047767</td>\n",
       "      <td>7.144473</td>\n",
       "      <td>2.786700</td>\n",
       "      <td>7.683000</td>\n",
       "      <td>1.612184</td>\n",
       "      <td>0.510900</td>\n",
       "      <td>5.110000</td>\n",
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       "      <td>2.031067</td>\n",
       "      <td>215.428683</td>\n",
       "      <td>26.313867</td>\n",
       "      <td>2.299467</td>\n",
       "      <td>1.582307</td>\n",
       "      <td>4.418761</td>\n",
       "      <td>2.507934</td>\n",
       "      <td>201449.680631</td>\n",
       "      <td>1.398133</td>\n",
       "      <td>18.062224</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21423.562842</td>\n",
       "      <td>18.009168</td>\n",
       "      <td>233.329093</td>\n",
       "      <td>4055.789248</td>\n",
       "      <td>4.383048</td>\n",
       "      <td>2.037599</td>\n",
       "      <td>9.294335</td>\n",
       "      <td>0.809380</td>\n",
       "      <td>0.796347</td>\n",
       "      <td>0.671204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.424548</td>\n",
       "      <td>196.882864</td>\n",
       "      <td>25.557234</td>\n",
       "      <td>1.718687</td>\n",
       "      <td>0.767380</td>\n",
       "      <td>0.614021</td>\n",
       "      <td>0.500200</td>\n",
       "      <td>282.230336</td>\n",
       "      <td>0.496486</td>\n",
       "      <td>629.444049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200606.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18622.750000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1171.000000</td>\n",
       "      <td>3.907500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>201211.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.100000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
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       "      <td>13.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>4920.500000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>201504.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.479900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55728.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>8976.000000</td>\n",
       "      <td>9.540000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>201702.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>74156.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>1248.000000</td>\n",
       "      <td>12735.000000</td>\n",
       "      <td>44.740000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1037.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>202105.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>109000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              carid         brand        serial         model       mileage  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean   37099.253567     19.228300    237.707733   5312.047767      7.144473   \n",
       "std    21423.562842     18.009168    233.329093   4055.789248      4.383048   \n",
       "min        1.000000      1.000000      1.000000      1.000000      0.010000   \n",
       "25%    18622.750000      8.000000     76.000000   1171.000000      3.907500   \n",
       "50%    36951.500000     13.000000    162.000000   4920.500000      6.540000   \n",
       "75%    55728.500000     23.000000    312.000000   8976.000000      9.540000   \n",
       "max    74156.000000    133.000000   1248.000000  12735.000000     44.740000   \n",
       "\n",
       "              color        cityId       carCode  transferCount      seatings  \\\n",
       "count  30000.000000  30000.000000  29991.000000   30000.000000  30000.000000   \n",
       "mean       2.786700      7.683000      1.612184       0.510900      5.110000   \n",
       "std        2.037599      9.294335      0.809380       0.796347      0.671204   \n",
       "min        1.000000      1.000000      1.000000       0.000000      2.000000   \n",
       "25%        1.000000      1.000000      1.000000       0.000000      5.000000   \n",
       "50%        2.000000      4.000000      1.000000       0.000000      5.000000   \n",
       "75%        5.000000     14.000000      2.000000       1.000000      5.000000   \n",
       "max       10.000000     93.000000      6.000000      10.000000      9.000000   \n",
       "\n",
       "       ...           af3           af4           af5           af6  \\\n",
       "count  ...  30000.000000  17892.000000  30000.000000  30000.000000   \n",
       "mean   ...      2.031067    215.428683     26.313867      2.299467   \n",
       "std    ...      0.424548    196.882864     25.557234      1.718687   \n",
       "min    ...      1.000000      1.000000      1.000000      1.000000   \n",
       "25%    ...      2.000000     68.000000      9.000000      1.000000   \n",
       "50%    ...      2.000000    156.000000     19.000000      2.000000   \n",
       "75%    ...      2.000000    310.000000     34.000000      2.000000   \n",
       "max    ...     11.000000   1037.000000    177.000000     15.000000   \n",
       "\n",
       "                af8           af9          af10           af13          af14  \\\n",
       "count  26225.000000  26256.000000  23759.000000   28381.000000  30000.000000   \n",
       "mean       1.582307      4.418761      2.507934  201449.680631      1.398133   \n",
       "std        0.767380      0.614021      0.500200     282.230336      0.496486   \n",
       "min        1.000000      2.000000      1.000000  200606.000000      1.000000   \n",
       "25%        1.000000      4.000000      2.000000  201211.000000      1.000000   \n",
       "50%        1.000000      4.000000      3.000000  201504.000000      1.000000   \n",
       "75%        2.000000      5.000000      3.000000  201702.000000      2.000000   \n",
       "max        8.000000      6.000000      3.000000  202105.000000      3.000000   \n",
       "\n",
       "               price  \n",
       "count   30000.000000  \n",
       "mean       18.062224  \n",
       "std       629.444049  \n",
       "min         0.050000  \n",
       "25%         6.100000  \n",
       "50%        10.479900  \n",
       "75%        18.000000  \n",
       "max    109000.000000  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.describe()  # 描述统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95ab6983",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.343123Z",
     "start_time": "2022-01-12T14:57:14.314163Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30000 entries, 0 to 29999\n",
      "Data columns (total 36 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   carid          30000 non-null  int64         \n",
      " 1   tradeTime      30000 non-null  datetime64[ns]\n",
      " 2   brand          30000 non-null  int64         \n",
      " 3   serial         30000 non-null  int64         \n",
      " 4   model          30000 non-null  int64         \n",
      " 5   mileage        30000 non-null  float64       \n",
      " 6   color          30000 non-null  int64         \n",
      " 7   cityId         30000 non-null  int64         \n",
      " 8   carCode        29991 non-null  float64       \n",
      " 9   transferCount  30000 non-null  int64         \n",
      " 10  seatings       30000 non-null  int64         \n",
      " 11  registerDate   30000 non-null  datetime64[ns]\n",
      " 12  lincenseDate   30000 non-null  datetime64[ns]\n",
      " 13  country        26243 non-null  float64       \n",
      " 14  maketype       26359 non-null  float64       \n",
      " 15  modelyear      29688 non-null  float64       \n",
      " 16  displacement   30000 non-null  float64       \n",
      " 17  gearbox        29999 non-null  float64       \n",
      " 18  oiltype        30000 non-null  int64         \n",
      " 19  newprice       30000 non-null  float64       \n",
      " 20  af1            28418 non-null  float64       \n",
      " 21  af2            30000 non-null  int64         \n",
      " 22  af3            30000 non-null  int64         \n",
      " 23  af4            17892 non-null  float64       \n",
      " 24  af5            30000 non-null  int64         \n",
      " 25  af6            30000 non-null  int64         \n",
      " 26  af7            11956 non-null  datetime64[ns]\n",
      " 27  af8            26225 non-null  float64       \n",
      " 28  af9            26256 non-null  float64       \n",
      " 29  af10           23759 non-null  float64       \n",
      " 30  af11           29539 non-null  object        \n",
      " 31  af12           30000 non-null  object        \n",
      " 32  af13           28381 non-null  float64       \n",
      " 33  af14           30000 non-null  int64         \n",
      " 34  af15           2420 non-null   datetime64[ns]\n",
      " 35  price          30000 non-null  float64       \n",
      "dtypes: datetime64[ns](5), float64(15), int64(14), object(2)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "x.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fab7ddeb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.359044Z",
     "start_time": "2022-01-12T14:57:14.344083Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "af15             91.93%\n",
       "af7              60.15%\n",
       "af4              40.36%\n",
       "af10             20.80%\n",
       "af8              12.58%\n",
       "country          12.52%\n",
       "af9              12.48%\n",
       "maketype         12.14%\n",
       "af13              5.40%\n",
       "af1               5.27%\n",
       "af11              1.54%\n",
       "modelyear         1.04%\n",
       "carCode           0.03%\n",
       "gearbox           0.00%\n",
       "af2               0.00%\n",
       "af6               0.00%\n",
       "af12              0.00%\n",
       "af5               0.00%\n",
       "af14              0.00%\n",
       "af3               0.00%\n",
       "carid             0.00%\n",
       "oiltype           0.00%\n",
       "newprice          0.00%\n",
       "tradeTime         0.00%\n",
       "displacement      0.00%\n",
       "lincenseDate      0.00%\n",
       "registerDate      0.00%\n",
       "seatings          0.00%\n",
       "transferCount     0.00%\n",
       "cityId            0.00%\n",
       "color             0.00%\n",
       "mileage           0.00%\n",
       "model             0.00%\n",
       "serial            0.00%\n",
       "brand             0.00%\n",
       "price             0.00%\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def loss_rate(x):  # 传入DF数据表\n",
    "    table = ((x.isnull().sum())/x.shape[0]).sort_values(ascending=False).map(lambda i:\"{:.2%}\".format(i))\n",
    "    return table\n",
    "#  缺失比例排序，变成百分比输出\n",
    "loss_rate(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b024ed8",
   "metadata": {},
   "source": [
    "# 数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e58faee1",
   "metadata": {},
   "source": [
    "## 处理缺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "939b30f2",
   "metadata": {},
   "source": [
    "> 该阶段无需进行标准化， 全部处理完再标准化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31837958",
   "metadata": {},
   "source": [
    "### 处理缺失量太大的字段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ca06248",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.375043Z",
     "start_time": "2022-01-12T14:57:14.361038Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp = x.drop(['af15', 'af7', 'af4'], axis=1)  # 删除缺失量达40%以上的变量"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d88f01df",
   "metadata": {},
   "source": [
    "> 删除变量不改变数据总量，后续操作将改变数据量，每删除一次，就会导致剩余的缺失值的比例扩大，因此要删一次分析一次"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9732051",
   "metadata": {},
   "source": [
    "### 处理af10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55b406b4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.390996Z",
     "start_time": "2022-01-12T14:57:14.375998Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6241"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dp['af10'].isnull().sum()  # af10缺失的有接近五分之一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b37e5e0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.406950Z",
     "start_time": "2022-01-12T14:57:14.391956Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【特征统计】：\n",
      "count    23759.000000\n",
      "mean         2.507934\n",
      "std          0.500200\n",
      "min          1.000000\n",
      "25%          2.000000\n",
      "50%          3.000000\n",
      "75%          3.000000\n",
      "max          3.000000\n",
      "Name: af10, dtype: float64\n",
      "\n",
      "【类别统计】：\n",
      "3.0    12071\n",
      "2.0    11685\n",
      "1.0        3\n",
      "Name: af10, dtype: int64\n",
      "\n",
      "【缺失比例】：0.208033\n",
      "【缺失数量】：6241\n"
     ]
    }
   ],
   "source": [
    "def some_info(x):  # 传入字段名称即可，字符串，调用该函数查看信息，作为处理决策依据\n",
    "    print('【特征统计】：')\n",
    "    print(x_dp[x].describe(), end='\\n\\n')\n",
    "    print('【类别统计】：')\n",
    "    print(x_dp[x].value_counts(),end='\\n\\n')\n",
    "    print('【缺失比例】：%f'%((x_dp[x].isnull().sum())/x_dp[x].shape[0]))\n",
    "    print('【缺失数量】：%d'%x_dp[x].isnull().sum())\n",
    "some_info('af10')\n",
    "#  这里我考虑用平均数填补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb27e8ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.422872Z",
     "start_time": "2022-01-12T14:57:14.407913Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp['af10'] = x_dp['af10'].fillna(x_dp['af10'].mean())  # 填补x_dp['af10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a49b930",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.438873Z",
     "start_time": "2022-01-12T14:57:14.425903Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "af8              12.58%\n",
       "country          12.52%\n",
       "af9              12.48%\n",
       "maketype         12.14%\n",
       "af13              5.40%\n",
       "af1               5.27%\n",
       "af11              1.54%\n",
       "modelyear         1.04%\n",
       "carCode           0.03%\n",
       "gearbox           0.00%\n",
       "af10              0.00%\n",
       "newprice          0.00%\n",
       "af6               0.00%\n",
       "af5               0.00%\n",
       "af3               0.00%\n",
       "af12              0.00%\n",
       "af14              0.00%\n",
       "af2               0.00%\n",
       "carid             0.00%\n",
       "displacement      0.00%\n",
       "oiltype           0.00%\n",
       "tradeTime         0.00%\n",
       "lincenseDate      0.00%\n",
       "registerDate      0.00%\n",
       "seatings          0.00%\n",
       "transferCount     0.00%\n",
       "cityId            0.00%\n",
       "color             0.00%\n",
       "mileage           0.00%\n",
       "model             0.00%\n",
       "serial            0.00%\n",
       "brand             0.00%\n",
       "price             0.00%\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_rate(x_dp)  # 可见af10已经全部填充"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0511267e",
   "metadata": {},
   "source": [
    "### 处理af8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c43f5f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.454817Z",
     "start_time": "2022-01-12T14:57:14.439847Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【特征统计】：\n",
      "count    26225.000000\n",
      "mean         1.582307\n",
      "std          0.767380\n",
      "min          1.000000\n",
      "25%          1.000000\n",
      "50%          1.000000\n",
      "75%          2.000000\n",
      "max          8.000000\n",
      "Name: af8, dtype: float64\n",
      "\n",
      "【类别统计】：\n",
      "1.0    13319\n",
      "2.0    12002\n",
      "4.0      454\n",
      "5.0      371\n",
      "6.0       65\n",
      "8.0       14\n",
      "Name: af8, dtype: int64\n",
      "\n",
      "【缺失比例】：0.125833\n",
      "【缺失数量】：3775\n"
     ]
    }
   ],
   "source": [
    "some_info('af8')  # af8 暂不好提出解决办法（类别比较多，并且分布不均匀），姑且用平均数填"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6901d41",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.470743Z",
     "start_time": "2022-01-12T14:57:14.455786Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp['af8'] = x_dp['af8'].fillna(x_dp['af8'].mean())  # 填补af8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d6fd7c8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.500703Z",
     "start_time": "2022-01-12T14:57:14.471740Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country          12.52%\n",
       "af9              12.48%\n",
       "maketype         12.14%\n",
       "af13              5.40%\n",
       "af1               5.27%\n",
       "af11              1.54%\n",
       "modelyear         1.04%\n",
       "carCode           0.03%\n",
       "gearbox           0.00%\n",
       "af3               0.00%\n",
       "af2               0.00%\n",
       "carid             0.00%\n",
       "af5               0.00%\n",
       "af6               0.00%\n",
       "newprice          0.00%\n",
       "af10              0.00%\n",
       "af12              0.00%\n",
       "af14              0.00%\n",
       "af8               0.00%\n",
       "displacement      0.00%\n",
       "oiltype           0.00%\n",
       "tradeTime         0.00%\n",
       "lincenseDate      0.00%\n",
       "registerDate      0.00%\n",
       "seatings          0.00%\n",
       "transferCount     0.00%\n",
       "cityId            0.00%\n",
       "color             0.00%\n",
       "mileage           0.00%\n",
       "model             0.00%\n",
       "serial            0.00%\n",
       "brand             0.00%\n",
       "price             0.00%\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_rate(x_dp)  # af8已经填补"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a980609a",
   "metadata": {},
   "source": [
    "### 处理country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e9976372",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.516620Z",
     "start_time": "2022-01-12T14:57:14.501663Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【特征统计】：\n",
      "count     26243.000000\n",
      "mean     777394.286667\n",
      "std       39624.220431\n",
      "min           0.000000\n",
      "25%      779412.000000\n",
      "50%      779413.000000\n",
      "75%      779415.000000\n",
      "max      779421.000000\n",
      "Name: country, dtype: float64\n",
      "\n",
      "【类别统计】：\n",
      "779412.0    10054\n",
      "779415.0     5809\n",
      "779416.0     3773\n",
      "779413.0     3094\n",
      "779419.0     1174\n",
      "779411.0      870\n",
      "779414.0      737\n",
      "779418.0      314\n",
      "779421.0      274\n",
      "779417.0       76\n",
      "0.0            68\n",
      "Name: country, dtype: int64\n",
      "\n",
      "【缺失比例】：0.125233\n",
      "【缺失数量】：3757\n"
     ]
    }
   ],
   "source": [
    "some_info('country')  # 'country'感觉只能删除缺失的，标记为0的也要删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1cf32263",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.532578Z",
     "start_time": "2022-01-12T14:57:14.517620Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp.dropna(subset=['country'], how='all', inplace=True)  # 不再保留删除空值之前的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "12beb4a4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.609373Z",
     "start_time": "2022-01-12T14:57:14.533576Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carid</th>\n",
       "      <th>brand</th>\n",
       "      <th>serial</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>color</th>\n",
       "      <th>cityId</th>\n",
       "      <th>carCode</th>\n",
       "      <th>transferCount</th>\n",
       "      <th>seatings</th>\n",
       "      <th>...</th>\n",
       "      <th>af2</th>\n",
       "      <th>af3</th>\n",
       "      <th>af5</th>\n",
       "      <th>af6</th>\n",
       "      <th>af8</th>\n",
       "      <th>af9</th>\n",
       "      <th>af10</th>\n",
       "      <th>af13</th>\n",
       "      <th>af14</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26236.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>26149.000000</td>\n",
       "      <td>26243.000000</td>\n",
       "      <td>24750.000000</td>\n",
       "      <td>26243.00000</td>\n",
       "      <td>26243.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37041.185764</td>\n",
       "      <td>18.651374</td>\n",
       "      <td>226.123804</td>\n",
       "      <td>5199.101970</td>\n",
       "      <td>7.604320</td>\n",
       "      <td>2.834203</td>\n",
       "      <td>7.593796</td>\n",
       "      <td>1.590601</td>\n",
       "      <td>0.529017</td>\n",
       "      <td>5.108143</td>\n",
       "      <td>...</td>\n",
       "      <td>5.243151</td>\n",
       "      <td>2.024426</td>\n",
       "      <td>25.681363</td>\n",
       "      <td>2.289449</td>\n",
       "      <td>1.581137</td>\n",
       "      <td>4.416383</td>\n",
       "      <td>2.509114</td>\n",
       "      <td>201412.184768</td>\n",
       "      <td>1.39321</td>\n",
       "      <td>17.687348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21444.625906</td>\n",
       "      <td>17.242950</td>\n",
       "      <td>224.910549</td>\n",
       "      <td>4069.665127</td>\n",
       "      <td>4.286582</td>\n",
       "      <td>2.056159</td>\n",
       "      <td>9.248213</td>\n",
       "      <td>0.732892</td>\n",
       "      <td>0.813363</td>\n",
       "      <td>0.668664</td>\n",
       "      <td>...</td>\n",
       "      <td>3.346350</td>\n",
       "      <td>0.389997</td>\n",
       "      <td>24.773918</td>\n",
       "      <td>1.688958</td>\n",
       "      <td>0.766595</td>\n",
       "      <td>0.614147</td>\n",
       "      <td>0.475306</td>\n",
       "      <td>268.958271</td>\n",
       "      <td>0.49552</td>\n",
       "      <td>672.916997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200606.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18538.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>4.480000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>201208.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>5.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36858.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>4730.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.507934</td>\n",
       "      <td>201410.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>9.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55786.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>8918.000000</td>\n",
       "      <td>9.910000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>201609.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>17.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>74156.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>1248.000000</td>\n",
       "      <td>12735.000000</td>\n",
       "      <td>44.740000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>202010.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>109000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              carid         brand        serial         model       mileage  \\\n",
       "count  26243.000000  26243.000000  26243.000000  26243.000000  26243.000000   \n",
       "mean   37041.185764     18.651374    226.123804   5199.101970      7.604320   \n",
       "std    21444.625906     17.242950    224.910549   4069.665127      4.286582   \n",
       "min        1.000000      1.000000      1.000000      1.000000      0.010000   \n",
       "25%    18538.000000      8.000000     71.000000   1084.000000      4.480000   \n",
       "50%    36858.000000     12.000000    156.000000   4730.000000      7.000000   \n",
       "75%    55786.500000     23.000000    290.000000   8918.000000      9.910000   \n",
       "max    74156.000000    133.000000   1248.000000  12735.000000     44.740000   \n",
       "\n",
       "              color        cityId       carCode  transferCount      seatings  \\\n",
       "count  26243.000000  26243.000000  26236.000000   26243.000000  26243.000000   \n",
       "mean       2.834203      7.593796      1.590601       0.529017      5.108143   \n",
       "std        2.056159      9.248213      0.732892       0.813363      0.668664   \n",
       "min        1.000000      1.000000      1.000000       0.000000      2.000000   \n",
       "25%        1.000000      1.000000      1.000000       0.000000      5.000000   \n",
       "50%        2.000000      4.000000      1.000000       0.000000      5.000000   \n",
       "75%        5.000000     14.000000      2.000000       1.000000      5.000000   \n",
       "max       10.000000     93.000000      6.000000      10.000000      9.000000   \n",
       "\n",
       "       ...           af2           af3           af5           af6  \\\n",
       "count  ...  26243.000000  26243.000000  26243.000000  26243.000000   \n",
       "mean   ...      5.243151      2.024426     25.681363      2.289449   \n",
       "std    ...      3.346350      0.389997     24.773918      1.688958   \n",
       "min    ...      1.000000      1.000000      1.000000      1.000000   \n",
       "25%    ...      2.000000      2.000000      8.000000      1.000000   \n",
       "50%    ...      5.000000      2.000000     19.000000      2.000000   \n",
       "75%    ...      6.000000      2.000000     31.000000      2.000000   \n",
       "max    ...     18.000000     11.000000    177.000000     15.000000   \n",
       "\n",
       "                af8           af9          af10           af13         af14  \\\n",
       "count  26243.000000  26149.000000  26243.000000   24750.000000  26243.00000   \n",
       "mean       1.581137      4.416383      2.509114  201412.184768      1.39321   \n",
       "std        0.766595      0.614147      0.475306     268.958271      0.49552   \n",
       "min        1.000000      2.000000      1.000000  200606.000000      1.00000   \n",
       "25%        1.000000      4.000000      2.000000  201208.000000      1.00000   \n",
       "50%        1.000000      4.000000      2.507934  201410.000000      1.00000   \n",
       "75%        2.000000      5.000000      3.000000  201609.000000      2.00000   \n",
       "max        8.000000      6.000000      3.000000  202010.000000      3.00000   \n",
       "\n",
       "               price  \n",
       "count   26243.000000  \n",
       "mean       17.687348  \n",
       "std       672.916997  \n",
       "min         0.050000  \n",
       "25%         5.880000  \n",
       "50%         9.880000  \n",
       "75%        17.300000  \n",
       "max    109000.000000  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fc8730c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.625373Z",
     "start_time": "2022-01-12T14:57:14.610371Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp.drop(x_dp[x_dp['country'] == 0].index, inplace=True)  # 不再保留包含0值的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "53abe4b4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.641327Z",
     "start_time": "2022-01-12T14:57:14.626334Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "af13             5.70%\n",
       "af1              5.00%\n",
       "af11             1.66%\n",
       "modelyear        1.10%\n",
       "af9              0.36%\n",
       "carCode          0.03%\n",
       "gearbox          0.00%\n",
       "af5              0.00%\n",
       "newprice         0.00%\n",
       "af2              0.00%\n",
       "af3              0.00%\n",
       "carid            0.00%\n",
       "af6              0.00%\n",
       "oiltype          0.00%\n",
       "af10             0.00%\n",
       "af12             0.00%\n",
       "af14             0.00%\n",
       "af8              0.00%\n",
       "displacement     0.00%\n",
       "tradeTime        0.00%\n",
       "maketype         0.00%\n",
       "country          0.00%\n",
       "lincenseDate     0.00%\n",
       "registerDate     0.00%\n",
       "seatings         0.00%\n",
       "transferCount    0.00%\n",
       "cityId           0.00%\n",
       "color            0.00%\n",
       "mileage          0.00%\n",
       "model            0.00%\n",
       "serial           0.00%\n",
       "brand            0.00%\n",
       "price            0.00%\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_rate(x_dp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4dc56b0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.737033Z",
     "start_time": "2022-01-12T14:57:14.642287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carid</th>\n",
       "      <th>brand</th>\n",
       "      <th>serial</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>color</th>\n",
       "      <th>cityId</th>\n",
       "      <th>carCode</th>\n",
       "      <th>transferCount</th>\n",
       "      <th>seatings</th>\n",
       "      <th>...</th>\n",
       "      <th>af2</th>\n",
       "      <th>af3</th>\n",
       "      <th>af5</th>\n",
       "      <th>af6</th>\n",
       "      <th>af8</th>\n",
       "      <th>af9</th>\n",
       "      <th>af10</th>\n",
       "      <th>af13</th>\n",
       "      <th>af14</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26168.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26081.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>24682.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "      <td>26175.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37001.973563</td>\n",
       "      <td>18.523171</td>\n",
       "      <td>225.791519</td>\n",
       "      <td>5201.105903</td>\n",
       "      <td>7.612594</td>\n",
       "      <td>2.832130</td>\n",
       "      <td>7.597287</td>\n",
       "      <td>1.591639</td>\n",
       "      <td>0.528940</td>\n",
       "      <td>5.108730</td>\n",
       "      <td>...</td>\n",
       "      <td>5.242407</td>\n",
       "      <td>2.024642</td>\n",
       "      <td>25.545444</td>\n",
       "      <td>2.292798</td>\n",
       "      <td>1.580278</td>\n",
       "      <td>4.417584</td>\n",
       "      <td>2.510165</td>\n",
       "      <td>201411.555222</td>\n",
       "      <td>1.393238</td>\n",
       "      <td>17.703281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21431.604716</td>\n",
       "      <td>17.080645</td>\n",
       "      <td>224.899605</td>\n",
       "      <td>4068.551107</td>\n",
       "      <td>4.286979</td>\n",
       "      <td>2.054228</td>\n",
       "      <td>9.241056</td>\n",
       "      <td>0.732818</td>\n",
       "      <td>0.813884</td>\n",
       "      <td>0.669231</td>\n",
       "      <td>...</td>\n",
       "      <td>3.349539</td>\n",
       "      <td>0.390296</td>\n",
       "      <td>24.661949</td>\n",
       "      <td>1.689869</td>\n",
       "      <td>0.767268</td>\n",
       "      <td>0.613283</td>\n",
       "      <td>0.475360</td>\n",
       "      <td>268.982865</td>\n",
       "      <td>0.495467</td>\n",
       "      <td>673.790436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200606.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18499.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1089.000000</td>\n",
       "      <td>4.490000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>201208.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36826.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>4731.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.507934</td>\n",
       "      <td>201410.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55731.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>8915.000000</td>\n",
       "      <td>9.930000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>201609.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>17.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>74156.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>1248.000000</td>\n",
       "      <td>12735.000000</td>\n",
       "      <td>44.740000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>202010.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>109000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              carid         brand        serial         model       mileage  \\\n",
       "count  26175.000000  26175.000000  26175.000000  26175.000000  26175.000000   \n",
       "mean   37001.973563     18.523171    225.791519   5201.105903      7.612594   \n",
       "std    21431.604716     17.080645    224.899605   4068.551107      4.286979   \n",
       "min        1.000000      1.000000      1.000000      1.000000      0.010000   \n",
       "25%    18499.000000      8.000000     71.000000   1089.000000      4.490000   \n",
       "50%    36826.000000     12.000000    154.000000   4731.000000      7.000000   \n",
       "75%    55731.500000     23.000000    290.000000   8915.000000      9.930000   \n",
       "max    74156.000000    133.000000   1248.000000  12735.000000     44.740000   \n",
       "\n",
       "              color        cityId       carCode  transferCount      seatings  \\\n",
       "count  26175.000000  26175.000000  26168.000000   26175.000000  26175.000000   \n",
       "mean       2.832130      7.597287      1.591639       0.528940      5.108730   \n",
       "std        2.054228      9.241056      0.732818       0.813884      0.669231   \n",
       "min        1.000000      1.000000      1.000000       0.000000      2.000000   \n",
       "25%        1.000000      1.000000      1.000000       0.000000      5.000000   \n",
       "50%        2.000000      4.000000      1.000000       0.000000      5.000000   \n",
       "75%        5.000000     14.000000      2.000000       1.000000      5.000000   \n",
       "max       10.000000     93.000000      6.000000      10.000000      9.000000   \n",
       "\n",
       "       ...           af2           af3           af5           af6  \\\n",
       "count  ...  26175.000000  26175.000000  26175.000000  26175.000000   \n",
       "mean   ...      5.242407      2.024642     25.545444      2.292798   \n",
       "std    ...      3.349539      0.390296     24.661949      1.689869   \n",
       "min    ...      1.000000      1.000000      1.000000      1.000000   \n",
       "25%    ...      2.000000      2.000000      8.000000      1.000000   \n",
       "50%    ...      5.000000      2.000000     19.000000      2.000000   \n",
       "75%    ...      6.000000      2.000000     31.000000      2.000000   \n",
       "max    ...     18.000000     11.000000    177.000000     15.000000   \n",
       "\n",
       "                af8           af9          af10           af13          af14  \\\n",
       "count  26175.000000  26081.000000  26175.000000   24682.000000  26175.000000   \n",
       "mean       1.580278      4.417584      2.510165  201411.555222      1.393238   \n",
       "std        0.767268      0.613283      0.475360     268.982865      0.495467   \n",
       "min        1.000000      2.000000      1.000000  200606.000000      1.000000   \n",
       "25%        1.000000      4.000000      2.000000  201208.000000      1.000000   \n",
       "50%        1.000000      4.000000      2.507934  201410.000000      1.000000   \n",
       "75%        2.000000      5.000000      3.000000  201609.000000      2.000000   \n",
       "max        8.000000      6.000000      3.000000  202010.000000      3.000000   \n",
       "\n",
       "               price  \n",
       "count   26175.000000  \n",
       "mean       17.703281  \n",
       "std       673.790436  \n",
       "min         0.050000  \n",
       "25%         5.880000  \n",
       "50%         9.800000  \n",
       "75%        17.300000  \n",
       "max    109000.000000  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dp.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde24069",
   "metadata": {},
   "source": [
    "### 处理af13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b6cf07b4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.767983Z",
     "start_time": "2022-01-12T14:57:14.739028Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【特征统计】：\n",
      "count     24682.000000\n",
      "mean     201411.555222\n",
      "std         268.982865\n",
      "min      200606.000000\n",
      "25%      201208.000000\n",
      "50%      201410.000000\n",
      "75%      201609.000000\n",
      "max      202010.000000\n",
      "Name: af13, dtype: float64\n",
      "\n",
      "【类别统计】：\n",
      "201609.0    555\n",
      "201604.0    491\n",
      "201202.0    468\n",
      "201603.0    468\n",
      "201309.0    429\n",
      "           ... \n",
      "200706.0      2\n",
      "200808.0      2\n",
      "200803.0      1\n",
      "200612.0      1\n",
      "202010.0      1\n",
      "Name: af13, Length: 160, dtype: int64\n",
      "\n",
      "【缺失比例】：0.057039\n",
      "【缺失数量】：1493\n"
     ]
    }
   ],
   "source": [
    "some_info('af13')  \n",
    "# 可见是年月信息，并且类别相当多（160个），考虑等距分组为顺序数据1，2，3，5等（无须改成热独编码，有顺序之分）\n",
    "# 数字越大代表距今时间越长"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "638c2d67",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:14.783937Z",
     "start_time": "2022-01-12T14:57:14.768949Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp.dropna(subset=['af13'], inplace=True) # 删除af13的缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d78e0426",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.290591Z",
     "start_time": "2022-01-12T14:57:14.784905Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_dp['af13'].hist()  # 考虑删除缺失数据（日期没有填补依据）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1a55e4f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.338463Z",
     "start_time": "2022-01-12T14:57:15.291552Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carid</th>\n",
       "      <th>tradeTime</th>\n",
       "      <th>brand</th>\n",
       "      <th>serial</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>color</th>\n",
       "      <th>cityId</th>\n",
       "      <th>carCode</th>\n",
       "      <th>transferCount</th>\n",
       "      <th>...</th>\n",
       "      <th>af5</th>\n",
       "      <th>af6</th>\n",
       "      <th>af8</th>\n",
       "      <th>af9</th>\n",
       "      <th>af10</th>\n",
       "      <th>af11</th>\n",
       "      <th>af12</th>\n",
       "      <th>af13</th>\n",
       "      <th>af14</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4220*1740*1625</td>\n",
       "      <td>201709.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-06-25</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8.60</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4630*1775*1480</td>\n",
       "      <td>201609.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.04</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4500*1834*1707</td>\n",
       "      <td>201608.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5.70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4315*1783*1606</td>\n",
       "      <td>201204.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>2021-06-16</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>7.03</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4150*1640*1535</td>\n",
       "      <td>201005.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29993</th>\n",
       "      <td>72533</td>\n",
       "      <td>2021-02-04</td>\n",
       "      <td>23</td>\n",
       "      <td>77</td>\n",
       "      <td>12481</td>\n",
       "      <td>6.87</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4725*1880*1686</td>\n",
       "      <td>201212.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29994</th>\n",
       "      <td>27237</td>\n",
       "      <td>2021-04-19</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>403</td>\n",
       "      <td>5.79</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4649*1859*1695</td>\n",
       "      <td>201511.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29996</th>\n",
       "      <td>4294</td>\n",
       "      <td>2020-06-03</td>\n",
       "      <td>12</td>\n",
       "      <td>92</td>\n",
       "      <td>1797</td>\n",
       "      <td>11.69</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4960*1845*1480</td>\n",
       "      <td>201201.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29997</th>\n",
       "      <td>20584</td>\n",
       "      <td>2020-09-27</td>\n",
       "      <td>7</td>\n",
       "      <td>113</td>\n",
       "      <td>501</td>\n",
       "      <td>9.87</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4665*1700*1510</td>\n",
       "      <td>200904.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29998</th>\n",
       "      <td>11490</td>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>45</td>\n",
       "      <td>131</td>\n",
       "      <td>3024</td>\n",
       "      <td>9.57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3+2</td>\n",
       "      <td>4915*1820*1450</td>\n",
       "      <td>201508.0</td>\n",
       "      <td>1</td>\n",
       "      <td>28.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24682 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carid  tradeTime  brand  serial  model  mileage  color  cityId  \\\n",
       "0          1 2021-06-28      1       1      1     4.01      1       1   \n",
       "1          2 2021-06-25      2       2      2     8.60      1       2   \n",
       "3          6 2021-06-29      6       6      6     6.04      1       3   \n",
       "4          7 2021-06-30      7       7      7     5.70      4       1   \n",
       "6         12 2021-06-16     10      12     12     7.03      5       3   \n",
       "...      ...        ...    ...     ...    ...      ...    ...     ...   \n",
       "29993  72533 2021-02-04     23      77  12481     6.87      1       1   \n",
       "29994  27237 2021-04-19     44     249    403     5.79      2       1   \n",
       "29996   4294 2020-06-03     12      92   1797    11.69      2       1   \n",
       "29997  20584 2020-09-27      7     113    501     9.87      6       1   \n",
       "29998  11490 2020-11-09     45     131   3024     9.57      1       1   \n",
       "\n",
       "       carCode  transferCount  ...  af5 af6  af8  af9  af10  af11  \\\n",
       "0          1.0              0  ...    1   1  1.0  5.0   2.0     1   \n",
       "1          1.0              0  ...    2   2  2.0  4.0   3.0   1+2   \n",
       "3          1.0              3  ...    6   1  2.0  5.0   2.0     1   \n",
       "4          2.0              2  ...    7   1  1.0  5.0   2.0     1   \n",
       "6          2.0              0  ...   11   4  1.0  5.0   3.0   NaN   \n",
       "...        ...            ...  ...  ...  ..  ...  ...   ...   ...   \n",
       "29993      2.0              1  ...   25   1  1.0  5.0   2.0   1+2   \n",
       "29994      1.0              0  ...   46   1  1.0  5.0   2.0   1+2   \n",
       "29996      2.0              2  ...   16   2  1.0  4.0   3.0   1+2   \n",
       "29997      2.0              0  ...    7   2  1.0  4.0   3.0     1   \n",
       "29998      1.0              1  ...   47   2  1.0  4.0   3.0   3+2   \n",
       "\n",
       "                 af12      af13  af14  price  \n",
       "0      4220*1740*1625  201709.0     1   4.24  \n",
       "1      4630*1775*1480  201609.0     2   7.38  \n",
       "3      4500*1834*1707  201608.0     2   4.38  \n",
       "4      4315*1783*1606  201204.0     1   5.90  \n",
       "6      4150*1640*1535  201005.0     2   0.30  \n",
       "...               ...       ...   ...    ...  \n",
       "29993  4725*1880*1686  201212.0     1  10.40  \n",
       "29994  4649*1859*1695  201511.0     1  11.80  \n",
       "29996  4960*1845*1480  201201.0     2   8.30  \n",
       "29997  4665*1700*1510  200904.0     2   5.00  \n",
       "29998  4915*1820*1450  201508.0     1  28.00  \n",
       "\n",
       "[24682 rows x 33 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "14fca569",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.354423Z",
     "start_time": "2022-01-12T14:57:15.339424Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "af1              5.10%\n",
       "af11             1.54%\n",
       "modelyear        1.02%\n",
       "af9              0.38%\n",
       "carCode          0.03%\n",
       "af6              0.00%\n",
       "af2              0.00%\n",
       "af3              0.00%\n",
       "af5              0.00%\n",
       "carid            0.00%\n",
       "af8              0.00%\n",
       "oiltype          0.00%\n",
       "af10             0.00%\n",
       "af12             0.00%\n",
       "af13             0.00%\n",
       "af14             0.00%\n",
       "newprice         0.00%\n",
       "displacement     0.00%\n",
       "gearbox          0.00%\n",
       "tradeTime        0.00%\n",
       "maketype         0.00%\n",
       "country          0.00%\n",
       "lincenseDate     0.00%\n",
       "registerDate     0.00%\n",
       "seatings         0.00%\n",
       "transferCount    0.00%\n",
       "cityId           0.00%\n",
       "color            0.00%\n",
       "mileage          0.00%\n",
       "model            0.00%\n",
       "serial           0.00%\n",
       "brand            0.00%\n",
       "price            0.00%\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_rate(x_dp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "557571d4",
   "metadata": {},
   "source": [
    "### 处理af1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9db7c59f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.369380Z",
     "start_time": "2022-01-12T14:57:15.355382Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【特征统计】：\n",
      "count    23422.000000\n",
      "mean         1.001452\n",
      "std          0.053863\n",
      "min          1.000000\n",
      "25%          1.000000\n",
      "50%          1.000000\n",
      "75%          1.000000\n",
      "max          3.000000\n",
      "Name: af1, dtype: float64\n",
      "\n",
      "【类别统计】：\n",
      "1.0    23405\n",
      "3.0       17\n",
      "Name: af1, dtype: int64\n",
      "\n",
      "【缺失比例】：0.051049\n",
      "【缺失数量】：1260\n"
     ]
    }
   ],
   "source": [
    "some_info('af1')  # 用众数填补（只有两类，直接填补感觉影响不大）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bacf470f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.385343Z",
     "start_time": "2022-01-12T14:57:15.371337Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp['af1'] = x_dp['af1'].fillna(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca5ab579",
   "metadata": {},
   "source": [
    "### 处理af11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b1cd6c8e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.401306Z",
     "start_time": "2022-01-12T14:57:15.386297Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【特征统计】：\n",
      "count     24302\n",
      "unique        5\n",
      "top         1+2\n",
      "freq      15770\n",
      "Name: af11, dtype: object\n",
      "\n",
      "【类别统计】：\n",
      "1+2        15770\n",
      "1           5138\n",
      "3+2         2760\n",
      "1+2,4+2      633\n",
      "5              1\n",
      "Name: af11, dtype: int64\n",
      "\n",
      "【缺失比例】：0.015396\n",
      "【缺失数量】：380\n"
     ]
    }
   ],
   "source": [
    "some_info('af11')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f45a6850",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.417215Z",
     "start_time": "2022-01-12T14:57:15.405248Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp.dropna(subset=['af11'], axis=0, inplace=True)  # 就地丢弃"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d514f06",
   "metadata": {},
   "source": [
    "### 处理modelyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f9b818a5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.433215Z",
     "start_time": "2022-01-12T14:57:15.418213Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【特征统计】：\n",
      "count    24052.000000\n",
      "mean      2014.483120\n",
      "std          2.694608\n",
      "min       2005.000000\n",
      "25%       2013.000000\n",
      "50%       2015.000000\n",
      "75%       2017.000000\n",
      "max       2021.000000\n",
      "Name: modelyear, dtype: float64\n",
      "\n",
      "【类别统计】：\n",
      "2016.0    3389\n",
      "2017.0    3249\n",
      "2015.0    2991\n",
      "2013.0    2735\n",
      "2014.0    2708\n",
      "2012.0    2106\n",
      "2018.0    1823\n",
      "2011.0    1820\n",
      "2010.0    1129\n",
      "2019.0     861\n",
      "2009.0     666\n",
      "2020.0     349\n",
      "2008.0     166\n",
      "2007.0      53\n",
      "2021.0       6\n",
      "2005.0       1\n",
      "Name: modelyear, dtype: int64\n",
      "\n",
      "【缺失比例】：0.010287\n",
      "【缺失数量】：250\n"
     ]
    }
   ],
   "source": [
    "some_info('modelyear')  # 年份没有填补依据，且缺失的比较少，直接删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f90282c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.449168Z",
     "start_time": "2022-01-12T14:57:15.434187Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp.dropna(subset = ['modelyear'], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d8e050",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T05:34:52.177813Z",
     "start_time": "2022-01-08T05:34:52.160859Z"
    }
   },
   "source": [
    "### 处理af9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "19b19c2d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.465087Z",
     "start_time": "2022-01-12T14:57:15.450129Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 24052 entries, 0 to 29998\n",
      "Data columns (total 33 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   carid          24052 non-null  int64         \n",
      " 1   tradeTime      24052 non-null  datetime64[ns]\n",
      " 2   brand          24052 non-null  int64         \n",
      " 3   serial         24052 non-null  int64         \n",
      " 4   model          24052 non-null  int64         \n",
      " 5   mileage        24052 non-null  float64       \n",
      " 6   color          24052 non-null  int64         \n",
      " 7   cityId         24052 non-null  int64         \n",
      " 8   carCode        24045 non-null  float64       \n",
      " 9   transferCount  24052 non-null  int64         \n",
      " 10  seatings       24052 non-null  int64         \n",
      " 11  registerDate   24052 non-null  datetime64[ns]\n",
      " 12  lincenseDate   24052 non-null  datetime64[ns]\n",
      " 13  country        24052 non-null  float64       \n",
      " 14  maketype       24052 non-null  float64       \n",
      " 15  modelyear      24052 non-null  float64       \n",
      " 16  displacement   24052 non-null  float64       \n",
      " 17  gearbox        24052 non-null  float64       \n",
      " 18  oiltype        24052 non-null  int64         \n",
      " 19  newprice       24052 non-null  float64       \n",
      " 20  af1            24052 non-null  float64       \n",
      " 21  af2            24052 non-null  int64         \n",
      " 22  af3            24052 non-null  int64         \n",
      " 23  af5            24052 non-null  int64         \n",
      " 24  af6            24052 non-null  int64         \n",
      " 25  af8            24052 non-null  float64       \n",
      " 26  af9            23979 non-null  float64       \n",
      " 27  af10           24052 non-null  float64       \n",
      " 28  af11           24052 non-null  object        \n",
      " 29  af12           24052 non-null  object        \n",
      " 30  af13           24052 non-null  float64       \n",
      " 31  af14           24052 non-null  int64         \n",
      " 32  price          24052 non-null  float64       \n",
      "dtypes: datetime64[ns](3), float64(14), int64(14), object(2)\n",
      "memory usage: 6.2+ MB\n"
     ]
    }
   ],
   "source": [
    "x_dp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6d50e24b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.481083Z",
     "start_time": "2022-01-12T14:57:15.466084Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【特征统计】：\n",
      "count    23979.000000\n",
      "mean         4.434422\n",
      "std          0.596079\n",
      "min          2.000000\n",
      "25%          4.000000\n",
      "50%          4.000000\n",
      "75%          5.000000\n",
      "max          6.000000\n",
      "Name: af9, dtype: float64\n",
      "\n",
      "【类别统计】：\n",
      "4.0    11930\n",
      "5.0    11383\n",
      "3.0      334\n",
      "2.0      324\n",
      "6.0        8\n",
      "Name: af9, dtype: int64\n",
      "\n",
      "【缺失比例】：0.003035\n",
      "【缺失数量】：73\n"
     ]
    }
   ],
   "source": [
    "some_info('af9')  # 缺失的少，删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "45454951",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.496042Z",
     "start_time": "2022-01-12T14:57:15.482065Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp.dropna(subset = ['af9'], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be1d06d0",
   "metadata": {},
   "source": [
    "### 处理carCode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b5e9cbff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.511999Z",
     "start_time": "2022-01-12T14:57:15.497002Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【特征统计】：\n",
      "count    23972.000000\n",
      "mean         1.537127\n",
      "std          0.699767\n",
      "min          1.000000\n",
      "25%          1.000000\n",
      "50%          1.000000\n",
      "75%          2.000000\n",
      "max          6.000000\n",
      "Name: carCode, dtype: float64\n",
      "\n",
      "【类别统计】：\n",
      "1.0    13084\n",
      "2.0     9793\n",
      "4.0      883\n",
      "3.0      208\n",
      "6.0        2\n",
      "5.0        2\n",
      "Name: carCode, dtype: int64\n",
      "\n",
      "【缺失比例】：0.000292\n",
      "【缺失数量】：7\n"
     ]
    }
   ],
   "source": [
    "some_info('carCode')  # 缺失的少，直接删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9019f372",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.527919Z",
     "start_time": "2022-01-12T14:57:15.512960Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp.dropna(subset = ['carCode'], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8a1cc7fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.543876Z",
     "start_time": "2022-01-12T14:57:15.528916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carid            0.00%\n",
       "gearbox          0.00%\n",
       "af14             0.00%\n",
       "af13             0.00%\n",
       "af12             0.00%\n",
       "af11             0.00%\n",
       "af10             0.00%\n",
       "af9              0.00%\n",
       "af8              0.00%\n",
       "af6              0.00%\n",
       "af5              0.00%\n",
       "af3              0.00%\n",
       "af2              0.00%\n",
       "af1              0.00%\n",
       "newprice         0.00%\n",
       "oiltype          0.00%\n",
       "displacement     0.00%\n",
       "tradeTime        0.00%\n",
       "modelyear        0.00%\n",
       "maketype         0.00%\n",
       "country          0.00%\n",
       "lincenseDate     0.00%\n",
       "registerDate     0.00%\n",
       "seatings         0.00%\n",
       "transferCount    0.00%\n",
       "carCode          0.00%\n",
       "cityId           0.00%\n",
       "color            0.00%\n",
       "mileage          0.00%\n",
       "model            0.00%\n",
       "serial           0.00%\n",
       "brand            0.00%\n",
       "price            0.00%\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_rate(x_dp)  # 无缺失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6d0cde6d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.623703Z",
     "start_time": "2022-01-12T14:57:15.545876Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carid</th>\n",
       "      <th>brand</th>\n",
       "      <th>serial</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>color</th>\n",
       "      <th>cityId</th>\n",
       "      <th>carCode</th>\n",
       "      <th>transferCount</th>\n",
       "      <th>seatings</th>\n",
       "      <th>...</th>\n",
       "      <th>af2</th>\n",
       "      <th>af3</th>\n",
       "      <th>af5</th>\n",
       "      <th>af6</th>\n",
       "      <th>af8</th>\n",
       "      <th>af9</th>\n",
       "      <th>af10</th>\n",
       "      <th>af13</th>\n",
       "      <th>af14</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "      <td>23972.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37007.019731</td>\n",
       "      <td>18.272943</td>\n",
       "      <td>220.621475</td>\n",
       "      <td>5144.857334</td>\n",
       "      <td>7.338553</td>\n",
       "      <td>2.777908</td>\n",
       "      <td>7.537586</td>\n",
       "      <td>1.537127</td>\n",
       "      <td>0.511722</td>\n",
       "      <td>5.119473</td>\n",
       "      <td>...</td>\n",
       "      <td>5.203237</td>\n",
       "      <td>2.026364</td>\n",
       "      <td>25.097864</td>\n",
       "      <td>2.255173</td>\n",
       "      <td>1.613451</td>\n",
       "      <td>4.434382</td>\n",
       "      <td>2.507389</td>\n",
       "      <td>201413.628191</td>\n",
       "      <td>1.391123</td>\n",
       "      <td>18.596214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21486.654119</td>\n",
       "      <td>16.760783</td>\n",
       "      <td>219.155485</td>\n",
       "      <td>4074.820439</td>\n",
       "      <td>4.006316</td>\n",
       "      <td>2.051113</td>\n",
       "      <td>9.242272</td>\n",
       "      <td>0.699767</td>\n",
       "      <td>0.783515</td>\n",
       "      <td>0.635923</td>\n",
       "      <td>...</td>\n",
       "      <td>3.305227</td>\n",
       "      <td>0.399164</td>\n",
       "      <td>24.136989</td>\n",
       "      <td>1.648850</td>\n",
       "      <td>0.777472</td>\n",
       "      <td>0.596031</td>\n",
       "      <td>0.474336</td>\n",
       "      <td>268.225714</td>\n",
       "      <td>0.495225</td>\n",
       "      <td>704.053156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200606.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18290.750000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1056.000000</td>\n",
       "      <td>4.390000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>201208.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36833.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>4581.500000</td>\n",
       "      <td>6.840000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.507934</td>\n",
       "      <td>201410.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55905.500000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>287.250000</td>\n",
       "      <td>8888.500000</td>\n",
       "      <td>9.570000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>201609.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>17.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>74156.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>1244.000000</td>\n",
       "      <td>12735.000000</td>\n",
       "      <td>36.580000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>202010.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>109000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              carid         brand        serial         model       mileage  \\\n",
       "count  23972.000000  23972.000000  23972.000000  23972.000000  23972.000000   \n",
       "mean   37007.019731     18.272943    220.621475   5144.857334      7.338553   \n",
       "std    21486.654119     16.760783    219.155485   4074.820439      4.006316   \n",
       "min        1.000000      1.000000      1.000000      1.000000      0.010000   \n",
       "25%    18290.750000      8.000000     71.000000   1056.000000      4.390000   \n",
       "50%    36833.500000     12.000000    153.000000   4581.500000      6.840000   \n",
       "75%    55905.500000     20.000000    287.250000   8888.500000      9.570000   \n",
       "max    74156.000000    131.000000   1244.000000  12735.000000     36.580000   \n",
       "\n",
       "              color        cityId       carCode  transferCount      seatings  \\\n",
       "count  23972.000000  23972.000000  23972.000000   23972.000000  23972.000000   \n",
       "mean       2.777908      7.537586      1.537127       0.511722      5.119473   \n",
       "std        2.051113      9.242272      0.699767       0.783515      0.635923   \n",
       "min        1.000000      1.000000      1.000000       0.000000      2.000000   \n",
       "25%        1.000000      1.000000      1.000000       0.000000      5.000000   \n",
       "50%        2.000000      4.000000      1.000000       0.000000      5.000000   \n",
       "75%        5.000000     14.000000      2.000000       1.000000      5.000000   \n",
       "max       10.000000     93.000000      6.000000      10.000000      9.000000   \n",
       "\n",
       "       ...           af2           af3           af5           af6  \\\n",
       "count  ...  23972.000000  23972.000000  23972.000000  23972.000000   \n",
       "mean   ...      5.203237      2.026364     25.097864      2.255173   \n",
       "std    ...      3.305227      0.399164     24.136989      1.648850   \n",
       "min    ...      1.000000      1.000000      1.000000      1.000000   \n",
       "25%    ...      2.000000      2.000000      8.000000      1.000000   \n",
       "50%    ...      5.000000      2.000000     19.000000      2.000000   \n",
       "75%    ...      6.000000      2.000000     30.000000      2.000000   \n",
       "max    ...     18.000000     11.000000    175.000000     15.000000   \n",
       "\n",
       "                af8           af9          af10           af13          af14  \\\n",
       "count  23972.000000  23972.000000  23972.000000   23972.000000  23972.000000   \n",
       "mean       1.613451      4.434382      2.507389  201413.628191      1.391123   \n",
       "std        0.777472      0.596031      0.474336     268.225714      0.495225   \n",
       "min        1.000000      2.000000      1.000000  200606.000000      1.000000   \n",
       "25%        1.000000      4.000000      2.000000  201208.000000      1.000000   \n",
       "50%        2.000000      4.000000      2.507934  201410.000000      1.000000   \n",
       "75%        2.000000      5.000000      3.000000  201609.000000      2.000000   \n",
       "max        8.000000      6.000000      3.000000  202010.000000      3.000000   \n",
       "\n",
       "               price  \n",
       "count   23972.000000  \n",
       "mean       18.596214  \n",
       "std       704.053156  \n",
       "min         0.050000  \n",
       "25%         6.380000  \n",
       "50%        10.500000  \n",
       "75%        17.800000  \n",
       "max    109000.000000  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dp.describe()  # 还剩23972个数据，剩余率 23972 / 30000 * 100% = 79.91% ，尚可接受"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ca921e8",
   "metadata": {},
   "source": [
    "## 处理分类信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9f25fadb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.655579Z",
     "start_time": "2022-01-12T14:57:15.624661Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>carid</th>\n",
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       "      <td>1</td>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>4220*1740*1625</td>\n",
       "      <td>201709.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-06-25</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8.60</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4630*1775*1480</td>\n",
       "      <td>201609.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.04</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>4500*1834*1707</td>\n",
       "      <td>201608.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5.70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>4315*1783*1606</td>\n",
       "      <td>201204.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13</td>\n",
       "      <td>2021-07-17</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.507934</td>\n",
       "      <td>1</td>\n",
       "      <td>4680*1785*1460</td>\n",
       "      <td>201808.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   carid  tradeTime  brand  serial  model  mileage  color  cityId  carCode  \\\n",
       "0      1 2021-06-28      1       1      1     4.01      1       1      1.0   \n",
       "1      2 2021-06-25      2       2      2     8.60      1       2      1.0   \n",
       "3      6 2021-06-29      6       6      6     6.04      1       3      1.0   \n",
       "4      7 2021-06-30      7       7      7     5.70      4       1      2.0   \n",
       "7     13 2021-07-17      6      13     13     4.64      1       3      1.0   \n",
       "\n",
       "   transferCount  ...  af5 af6  af8  af9      af10  af11            af12  \\\n",
       "0              0  ...    1   1  1.0  5.0  2.000000     1  4220*1740*1625   \n",
       "1              0  ...    2   2  2.0  4.0  3.000000   1+2  4630*1775*1480   \n",
       "3              3  ...    6   1  2.0  5.0  2.000000     1  4500*1834*1707   \n",
       "4              2  ...    7   1  1.0  5.0  2.000000     1  4315*1783*1606   \n",
       "7              0  ...    6   2  2.0  4.0  2.507934     1  4680*1785*1460   \n",
       "\n",
       "       af13  af14  price  \n",
       "0  201709.0     1   4.24  \n",
       "1  201609.0     2   7.38  \n",
       "3  201608.0     2   4.38  \n",
       "4  201204.0     1   5.90  \n",
       "7  201808.0     1   5.95  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab40906",
   "metadata": {},
   "source": [
    "### 处理时间数据'af13'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bcc0e6d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.671573Z",
     "start_time": "2022-01-12T14:57:15.656577Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp['af13'] = (x_dp['af13'][0:].max() - \n",
    "                x_dp['af13'][0:]).astype('int32')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decaaf28",
   "metadata": {},
   "source": [
    "### 处理时间数据'tradeTime'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "561726f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.907945Z",
     "start_time": "2022-01-12T14:57:15.672534Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']  # 正常显示中文\n",
    "x_dp['tradeTime'].hist(bins=50, figsize=(8,5))  # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2fad64a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:15.923860Z",
     "start_time": "2022-01-12T14:57:15.908902Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-01-01 00:00:00')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dp['tradeTime'][0:].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ec20a9d",
   "metadata": {},
   "source": [
    "> 用2.1.5中处理af13统一约定的时间处理方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0ec480ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:16.131306Z",
     "start_time": "2022-01-12T14:57:15.924860Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp['tradeTime'] = (x_dp['tradeTime'][0:].max() - \n",
    "                     x_dp['tradeTime'][0:]).astype('str').apply(lambda x:x[:-5]).astype('int32')\n",
    "# 日期相减是Timedelta格式，先转化为字符串，然后切片取数字部分，最后转整形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c0a9ef22",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:16.304860Z",
     "start_time": "2022-01-12T14:57:16.132303Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_dp['tradeTime'].hist(bins=50, figsize=(8,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a83c8d1c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T06:06:36.299745Z",
     "start_time": "2022-01-08T06:06:36.281793Z"
    }
   },
   "source": [
    "### 处理时间数据'registerDate'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7920b87f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:16.495395Z",
     "start_time": "2022-01-12T14:57:16.305841Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAExCAYAAABcVoroAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYcklEQVR4nO3df5Bd5X3f8fe3yGCJJUpUxCoIjNykwZhurbE2qSeGshtDPFjTNCYeg8ENIiFiXGJoq3YG1552/Ic1SlrsegpuIqDW1BBvVKZy4yq4FnWWaWbCUDRgL1AkHLoGKRjH4GJWRk0WffvHPchXy/689+7d5577fs3szD3P+XGf79zd89nnueeeG5mJJEkq199Y6Q5IkqT5GdaSJBXOsJYkqXCGtSRJhTOsJUkqnGEtSVLhVq10B+Zy9tln56ZNm04uHzt2jDPPPHPlOrSM6lwb1Ls+a+tdda7P2nrTwYMHv5+Z62dbV2xYb9q0iUcfffTk8vj4OCMjIyvXoWVU59qg3vVZW++qc33W1psi4jtzrXMaXJKkwhnWkiQVzrCWJKlwhrUkSYUzrCVJKpxhLUlS4QxrSZIKZ1hLklQ4w1qSpMIZ1pIkFc6wliSpcMXeG1yS6mrTbfvnXT+5a2uXeqJe4chakqTCGdaSJBXOsJYkqXCGtSRJhTOsJUkqnGEtSVLhDGtJkgq3YFhHxBkR8bGI2NfUtjYixiLimYh4IiL+ftO6XRFxJCImImJL1bYqIvZExNGIeDgi3r485UiSVD+LGVkfAn4ZOKup7W3Af8jMvw18HLgbICJ+CbgE2AT8M+CeavtfB94KnFe1fa4DfZckqS8sJqw3A59vbsjMicx8qFp8FFhfPb4K2JOZ05l5AFgfERuq9rszM4H7gMs70XlJkvpBNPJzgY0iRoBPZeabQjYiPg2cn5m/ERH/FbijCmoi4hHgJmAP8KHMfKZq/x5wYWb+YMaxtgPbAQYHB7eMjY2dXDc1NcXAwEALJZavzrVBveuztt61kvVNHH1l3vVDG9e2dfw6v3Z1rm10dPRgZg7Ptq7le4NHxCrgs8DfAf5h1Xw6cKJpsxPA6/O0nyIzdwO7AYaHh3NkZOTkuvHxcZqX66TOtUG967O23rWS9W1b6N7g1420dfw6v3Z1rm0+LV0NHhEB/BfgGPDLmflqteoFYGPTpucCR5rbI2I1sCozf9hqpyVJ6ietfnTrauAvM/MTmTnd1L4fuD4iTouIK4DDmfly1X5Dtc1Hga+02mFJkvpNq9Pgm4FfiYhvN7X9KrAPuAx4FngJuLZadyfwxYh4vlp3dYvPK0lS31lUWGfmODDetHwbcNscm99S/TTvfxz4SEs9lCSpz7V8gZkk9atNC10gtmtrl3qifuHtRiVJKpwja0nqMQuN7HcMTTPSna6oSxxZS5JUOMNakqTCGdaSJBXOsJYkqXBeYCap72y6bT87hqbnvEe3H71SaRxZS5JUOMNakqTCGdaSJBXOsJYkqXCGtSRJhTOsJUkqnGEtSVLhDGtJkgpnWEuSVDjDWpKkwhnWkiQVzrCWJKlwhrUkSYUzrCVJKpxhLUlS4QxrSZIKZ1hLklQ4w1qSpMIZ1pIkFc6wliSpcIa1JEmFM6wlSSqcYS1JUuEMa0mSCrdgWEfEGRHxsYjYN6P91oh4LiIORcSVTe27IuJIRExExJaqbVVE7ImIoxHxcES8vfOlSJJUT6sWsc0h4DHgrDcaIuJngJuBi4HzgQcj4gLgUuASYBMwCtwDbAZ+HXgrcB5wI/A54Fc7U4IkSfW2mGnwzcDnZ7R9ENibma9m5lPAJLAFuArYk5nTmXkAWB8RG6r2uzMzgfuAyzvUf0mSai8a+bnARhEjwKcy8/Jq+fPAE5l5V7W8F/gysA24owpqIuIR4CZgD/ChzHymav8ecGFm/mDG82wHtgMMDg5uGRsbO7luamqKgYGB1istWJ1rg3rXZ229aeLoKwyuhhdfm3390Ma1C+4/n5Xef3A1nLNu/mP0qjr/Xo6Ojh7MzOHZ1i1mGnw2pwMnmpZPAK+30H6KzNwN7AYYHh7OkZGRk+vGx8dpXq6TOtcG9a7P2nrTttv2s2NomtsnZj8FTl43suD+81np/XcMTfPhmr52df69nE+rV4O/AGxsWj4PeH6W9nOBI83tEbEaWJWZP2zxuSVJ6iuthvV+4JqIWBMRFwHrgMer9usj4rSIuAI4nJkvV+03VPt+FPhKW72WJKmPtDQNnpkHI+Je4EngOHBjZmb18a7LgGeBl4Brq13uBL4YEc9X665uu+eSJPWJRYV1Zo4D4zPadgI7Z7SdAG6pfprbjwMfaaOfkiT1rVYvMJPUpzYtdHHUrq1d6onUP7zdqCRJhTOsJUkqnGEtSVLhfM9akjpsoff1paVyZC1JUuEcWUvqKq8ml5bOkbUkSYUzrCVJKpxhLUlS4QxrSZIKZ1hLklQ4w1qSpML50S1Jp3jjo1U7hqbZ5s09pCIY1pJqxzuIqW6cBpckqXCGtSRJhTOsJUkqnGEtSVLhDGtJkgpnWEuSVDjDWpKkwhnWkiQVzrCWJKlw3sFMUk/x7mTqR46sJUkqnGEtSVLhDGtJkgpnWEuSVDjDWpKkwhnWkiQVzrCWJKlwbYV1RPxmRDxR/Wyr2m6NiOci4lBEXNm07a6IOBIRExGxpc1+S5LUN1q+KUpE/CTwSeDv0gj9b0bEBHAzcDFwPvBgRFwAXApcAmwCRoF7gM1t9FuSpL7Rzsj6NeAVYA2wGvgB8D5gb2a+mplPAZPAFuAqYE9mTmfmAWB9RGxoq+eSJPWJlsM6M/8fcDeNQJ4E7gI2At9p2uwI8NM0RtnN7UerdkmStIDIzNZ2jHg3jbC+HDgN+AbwfeDezLyn2mYM+APgY8C/zcz/UbU/DGzPzG/NOOZ2YDvA4ODglrGxsZPrpqamGBgYaKmvpatzbVDv+upY28TRVwAYXA0vvrb0/Yc2rl3U8Zdr/8War75u9WEu7T7/4Go4Z938x+hVdfybe8Po6OjBzByebV07X+RxOfC1zHwZICK+BrxKY3T9hvOA54EXZrSfS2PUfYrM3A3sBhgeHs6RkZGT68bHx2lerpM61wb1rq+OtW2rvihjx9A0t08s/RQxed3Ioo6/XPsv1nz1dasPc2n3+XcMTfPhmv1evqGOf3OL0c571k8DoxHx1ogYoPF+9evANRGxJiIuAtYBjwP7gesj4rSIuAI4/EbIS5Kk+bU8ss7MP4qIIeBQ1fSlzPxMRCTwJHAcuDEzMyL2AZcBzwIvAde22W9JkvpGW99nnZmfAT4zo20nsHNG2wnglupHkjQPv7NbM3kHM0mSCmdYS5JUOMNakqTCGdaSJBWurQvMJJVnoYuTJndt7VJPepcXeKk0jqwlSSqcYS1JUuEMa0mSCmdYS5JUOMNakqTCGdaSJBXOsJYkqXB+zlpSR/kZZanzHFlLklQ4R9ZSQRYzKvUOZFL/MaylDuqFW306TS31HqfBJUkqnGEtSVLhnAaXpD7UC2/Z6MccWUuSVDhH1pJUQ15IWC+GtaSiGDLSmzkNLklS4QxrSZIKZ1hLklQ4w1qSpMIZ1pIkFc6wliSpcIa1JEmFM6wlSSqcYS1JUuEMa0mSCmdYS5JUuLbuDR4Ra4HfBy4FjgMXAR8DdgCvAf8kMx+ott0FfBT4AbAtMw+289xSv/Le2VL/afeLPP498ATwEeAM4HzgZuDi6vGDEXEBjTC/BNgEjAL3AJvbfG5JkvpCy9PgEbEB+EVgZzYcBz4I7M3MVzPzKWAS2AJcBezJzOnMPACsr/aXJEkLiMxsbceI9wG3AVPAO4GvAm8BnsjMu6pt9gJfBrYBd1RBTUQ8AtyUmY/NOOZ2YDvA4ODglrGxsZPrpqamGBgYaKmvpatzbVDv+mbWNnH0lXm3H9q4dt71C+3fTYOr4cXXVroXy6fO9XWitoV+V1dKnc8no6OjBzNzeLZ17UyDn0MjpP8ejfehHwQ2AN9q2uYE8DpwevV4ZvspMnM3sBtgeHg4R0ZGTq4bHx+neblO6lwb1Lu+mbVtW+D95MnrRuZdv9D+3bRjaJrbJ+r7lfd1rq8TtS30u7pS6nw+mU87r+b3gIOZeQQgIg7QCOCNTducBzwPvDCj/VzgSBvPLUlS32jno1sPA++MiHMj4gzgchpT4tdExJqIuAhYBzwO7Aeuj4jTIuIK4HBmvtxm3yVJ6gstj6wz81hEfBw4QONK8D2ZeXsV3E/S+CjXjZmZEbEPuAx4FngJuLb9rkuS1B/aelOj+gz1AzPadgI7Z7SdAG6pfiRJ0hJ4BzNJkgpXz0shpRYtdHewyV1bu9QTSfoxR9aSJBXOsJYkqXCGtSRJhTOsJUkqnGEtSVLhDGtJkgpnWEuSVDjDWpKkwhnWkiQVzrCWJKlwhrUkSYUzrCVJKpxhLUlS4QxrSZIKZ1hLklQ4w1qSpMIZ1pIkFc6wliSpcKtWugNSP9l02/6V7oKkHuTIWpKkwjmyliS9yUKzQJO7tnapJwJH1pIkFc+wliSpcIa1JEmFM6wlSSqcYS1JUuG8GlxagplXyO4Ymmabn51WH/Jq8e5yZC1JUuEMa0mSCuc0uGrFqTlJddT2yDoi/jgi7q4e3xoRz0XEoYi4smmbXRFxJCImImJLu88pSVI/aWtkHRHvBzYDfxERPwPcDFwMnA88GBEXAJcClwCbgFHgnmofSZK0CC2PrCPiTODTwO9WTR8E9mbmq5n5FDAJbAGuAvZk5nRmHgDWR8SG9rotSVL/iMxsbcfG1PfXgTU0Rs7HgCcy865q/V7gy8A24I4qqImIR4CbMvOxWY65HdgOMDg4uGVsbOzkuqmpKQYGBlrqa+nqXBt0t76Jo6/Mu35o49q29p9pcDW8+NqSdukZda4N6l1fCbUt9LfWqjqfL0dHRw9m5vBs61qaBo+I64HMzL0Rsa1qPh040bTZCeD1edrfJDN3A7sBhoeHc2Rk5OS68fFxmpfrpM61QXfrW/AzzxPHFjjC0v4kdgxNc/tEPa/TrHNtUO/6Sqht8rqRZTlu3c+Xc2n11fw48JMR8TSwFlgN/ATwQtM25wHPV20bm9rPBY60+LySJPWdlt6zzszhzPzZzHwH8AngfuDngWsiYk1EXASsAx4H9gPXR8RpEXEFcDgzX+5M9yVJqr+OzZNk5sGIuBd4EjgO3JiZGRH7gMuAZ4GXgGs79ZySJPWDtsM6M/cAe6rHO4GdM9afAG6pfiRJ0hJ5u1FJkgpnWEuSVLh6fm5BtbXQvb8lqY4cWUuSVDhH1iqKI2dJejNH1pIkFc6wliSpcIa1JEmFM6wlSSqcYS1JUuEMa0mSCmdYS5JUOMNakqTCGdaSJBXOsJYkqXCGtSRJhfPe4Ooa7/stSa1xZC1JUuEcWUuSOm6hmbTJXVu71JN6cGQtSVLhHFlLkrrOkffSOLKWJKlwjqzVMXP9p7xjaJptXgkuSS1zZC1JUuEMa0mSCmdYS5JUOMNakqTCGdaSJBXOsJYkqXCGtSRJhTOsJUkqnGEtSVLhWg7riDg9Ir4QEYcj4pmI+LWq/daIeC4iDkXElU3b74qIIxExERFbOtF5SZL6QTu3G10HfCMz/3FE/BzwSEQ8AdwMXAycDzwYERcAlwKXAJuAUeAeYHMbzy1JUt9oOawz87vA/dXjwxExDVwD7M3MV4GnImIS2AJcBezJzGngQESsj4gN1TEkSTrFQt810G/fyhWZ2f5BIm4A/hEwATyRmXdV7XuBLwPbgDsy80DV/ghwU2Y+NuM424HtAIODg1vGxsZOrpuammJgYKDtvpaoLrVNHH1l1vbB1fDia13uTJdYW++qc339UNvQxrUr3ZWOGx0dPZiZw7Ota/tbtyLiNuBq4APAvwJONK0+AbwOnD5H+ykyczewG2B4eDhHRkZOrhsfH6d5uU7qUttc36y1Y2ia2yfq+QVv1ta76lxfP9Q2ed3ISnelq9p6NSPiTuBM4L2Z+aOIeAHY2LTJecDzwMz2c4Ej7Ty3JEn9op2rwd8DXJiZ2zLzR1XzfuCaiFgTERfRuAjt8ar9+og4LSKuAA5n5stt9l2SpL7Qzsh6MzAcEd9uavtt4F7gSeA4cGNmZkTsAy4DngVeAq5t43klSeor7VwN/nvA782y6mvAzhnbngBuqX4kSdISeAczSZIKZ1hLklQ4w1qSpMIZ1pIkFa6en5rXspjr9n+SpOXlyFqSpMIZ1pIkFc6wliSpcL5nrZN8T1qSyuTIWpKkwhnWkiQVzmlwSVLPWehtu8ldW7vUk+5wZC1JUuEMa0mSCmdYS5JUOMNakqTCGdaSJBXOsJYkqXCGtSRJhTOsJUkqnGEtSVLhvIOZJKl2FvPFRL10lzNH1pIkFc6wliSpcE6D9xG/r1qSepMja0mSCufIWpLUl3rpazYN6xpxmluS6slpcEmSCmdYS5JUOKfBe4jT3JLUnwzrghjGkqTZOA0uSVLhujayjogPA78DvA7szMz/2K3nLsF8o+YdQ9Nsc1QtSZpDV8I6Is4CbgfeQyOsH4+Ir2bmX3bj+bvBKWxJqpeSPofdrZH1+4GHMvMoQER8A3gfMNal51+QYStJKlVk5vI/ScQ/Bc7OzE9Wy78LvJCZn5ux3XZge7V4IXCoafXZwPeXvbMro861Qb3rs7beVef6rK03XZCZ62db0a2R9enAiablEzSmw0+RmbuB3bMdICIezczh5eneyqpzbVDv+qytd9W5Pmurn25dDf4CsLFp+Tzg+S49tyRJPa1bYf3fgfdHxDkRsQH4ReDrXXpuSZJ6WlemwTPzxYj4JPBnVdOOzDy2xMPMOj1eE3WuDepdn7X1rjrXZ20105ULzCRJUuu8g5kkSYUzrCVJKtyKhnVEnB4RX4iIwxHxTET8WtV+a0Q8FxGHIuLKpu13RcSRiJiIiC1N7ZdExLciYjIiPjfbc3VbJ2qLiDMj4g8i4n9HxEMR8bdWqp5mLdR2dkT8u4j4l01tqyJiT0QcjYiHI+LtK1HLbDpU3zsj4n9GxJ9HxJ9ExMbZnqvbOlFb07q3RcRrEXFJN2uYS6dqK/F8Ah37vez5c0pEbIyIByLi2xHxvyLi4qq92HNKR2Tmiv0AG4APVY9/Dvi/NG6Gchg4C3gn8BfAW4BfAv6UxkVxVwCPV/v9FPB/gHdVy6tXsqYO1/Zx4LPV4w8C9690XS3U9m7gz4E/AT7VdIzfoHEHuwB+C/jKStfV4fp+BXhb9fjTwO6VrqtTtTUd6z7gu8AlK11XB1+3Is8nHayvDueUy4ChatsbgK9Xj4s9p3TiZ0VH1pn53cy8v3p8GJgGrgH2ZuarmfkUMAlsAa4C9mTmdGYeANZH42Ngvwncm5nfrI7z2gqU8iYdqu054KyICOAngO+sQClvssTaDgHvAB6acZirgLuz8Vd2H3B5l7q/oE7Ul5l/lJnPVYuPArPelajbOvTaEREfAKaAp7vU9QV1qLYizyfQsfp6/pySmQ9l5kS1a/PfVrHnlE4o5j3riLgB+BawjlN/gY4APw2cP6P9aNX+LmBNRByMiG9GxBVd6vKitVHbAzT+o3wJ+Gz1U5SFasvMY5n517PserLmzPwR8KOI+Knl7u9StVHfG/u/BbgV2LesHW1Bq7VFxNk0Zgs+0ZWOtqCN16348wm0VV/Pn1NmbL6DH/9t9cQ5pVVFhHVE3AbcAlzH3Lcmnav9HBpTV79AY+rjSxFRRF3Qdm2/A3wjM9fRmO65vxt9XqxF1jaXpW7fdW3WR0ScAzwIPJaZ/2m5+tmKVmuLiNOAe4F/npkvL3c/W9Hm61b0+QTarq8W55Tqvff/DJwB7KzWF39OaceK/xJGxJ00pmvem5kvMPetSWe2n0vjP63vAf8tM1/PzEeAvwL+Zjf6vpAO1PZ+4EvQmFYFfjYi1nWh6wtaQm1zObl9RKwGVmXmD5epu0vWbn0RMUgjqL+Qmf9iOfu6VG3W9gvAZuD3I+Lpavm+iPgHy9fjxevA72Wx5xPoSH09f06pzhcPAn+amR/JzOlqfdHnlHat9NXg7wEuzMxt1bQFwH7gmohYExEX0ZgKebxqvz4iTqumpg5X/9k/AHwoGt4N/FUW8D3ZHartaWBrdbz3An8NvNLlUt5kibXNZT+N/+wBPgp8ZZm6u2Qdqu/TwL/JzD9c3t4uTbu1ZeafZeaGzHxHZr4DeAS4LjO/2o3+z6dDr1uR5xPoWH11OKfcCvxxZn5+xmGKPad0Qre+dWsum4HhiPh2U9tv05hmexI4DtyYmRkR+2hcBfgsjfdbrq22/0PgPcAzwA9pTJ+UYDPt1/ZbwFhE3Aq8ClyTmSVM62xmkbXNc4w7gS9GxPM06r56mfrais20X99m4AMR8a+r5WOZ+a5l6OtSbab92kq1mfZrK/V8Ap2pr+fPKRGxGbgsIq5v2vbnKfuc0jZvNypJUuFW/D1rSZI0P8NakqTCGdaSJBXOsJYkqXCGtSRJhTOsJUkqnGEtSVLhDGtJkgpnWEuSVLj/D6yIVuo85/mSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_dp['registerDate'].hist(bins=50, figsize=(8,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8af8f07b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:16.669922Z",
     "start_time": "2022-01-12T14:57:16.497328Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp['registerDate'] = (x_dp['registerDate'][0:].max() - \n",
    "                        x_dp['registerDate'][0:]).astype('str').apply(lambda x:x[:-5]).astype('int32')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "536f6bf0",
   "metadata": {},
   "source": [
    "### 处理时间数据'lincenseDate'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6717e10e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:16.858725Z",
     "start_time": "2022-01-12T14:57:16.671865Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_dp['lincenseDate'].hist(bins=50, figsize=(8,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "16262a01",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:17.033008Z",
     "start_time": "2022-01-12T14:57:16.860715Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp['lincenseDate'] = (x_dp['lincenseDate'][0:].max() - \n",
    "                        x_dp['lincenseDate'][0:]).astype('str').apply(lambda x:x[:-5]).astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8152e23d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:17.206430Z",
     "start_time": "2022-01-12T14:57:17.034588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_dp['lincenseDate'].hist(bins=50, figsize=(8,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1636aac9",
   "metadata": {},
   "source": [
    "## 处理（转化）分类数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dca45e7",
   "metadata": {},
   "source": [
    "### 剔除price字段异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a01d1725",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:17.426779Z",
     "start_time": "2022-01-12T14:57:17.208124Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_dp['price'].hist(bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f92f4e",
   "metadata": {},
   "source": [
    "> price中有异常值,否则范围不会拉到这么大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8da4f943",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:17.855446Z",
     "start_time": "2022-01-12T14:57:17.427538Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "std_scl = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9c43128f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:17.871381Z",
     "start_time": "2022-01-12T14:57:17.856392Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     23972.000000\n",
       "mean         18.596214\n",
       "std         704.053156\n",
       "min           0.050000\n",
       "25%           6.380000\n",
       "50%          10.500000\n",
       "75%          17.800000\n",
       "max      109000.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dp['price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f8fda180",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:17.902306Z",
     "start_time": "2022-01-12T14:57:17.872348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carid</th>\n",
       "      <th>tradeTime</th>\n",
       "      <th>brand</th>\n",
       "      <th>serial</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>color</th>\n",
       "      <th>cityId</th>\n",
       "      <th>carCode</th>\n",
       "      <th>transferCount</th>\n",
       "      <th>...</th>\n",
       "      <th>af5</th>\n",
       "      <th>af6</th>\n",
       "      <th>af8</th>\n",
       "      <th>af9</th>\n",
       "      <th>af10</th>\n",
       "      <th>af11</th>\n",
       "      <th>af12</th>\n",
       "      <th>af13</th>\n",
       "      <th>af14</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7793</th>\n",
       "      <td>54999</td>\n",
       "      <td>320</td>\n",
       "      <td>8</td>\n",
       "      <td>208</td>\n",
       "      <td>9962</td>\n",
       "      <td>6.15</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4199*1786*1479</td>\n",
       "      <td>808</td>\n",
       "      <td>1</td>\n",
       "      <td>658.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22115</th>\n",
       "      <td>49878</td>\n",
       "      <td>478</td>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "      <td>695</td>\n",
       "      <td>4.52</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4633*1840*1530</td>\n",
       "      <td>404</td>\n",
       "      <td>2</td>\n",
       "      <td>109000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27461</th>\n",
       "      <td>23427</td>\n",
       "      <td>113</td>\n",
       "      <td>100</td>\n",
       "      <td>947</td>\n",
       "      <td>5008</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.507934</td>\n",
       "      <td>3+2</td>\n",
       "      <td>5825*1926*1541</td>\n",
       "      <td>307</td>\n",
       "      <td>2</td>\n",
       "      <td>596.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carid  tradeTime  brand  serial  model  mileage  color  cityId  \\\n",
       "7793   54999        320      8     208   9962     6.15      3      19   \n",
       "22115  49878        478     16      37    695     4.52      8       5   \n",
       "27461  23427        113    100     947   5008     0.43      2       1   \n",
       "\n",
       "       carCode  transferCount  ...  af5  af6  af8  af9      af10  af11  \\\n",
       "7793       2.0              1  ...    8    3  2.0  5.0  2.000000   1+2   \n",
       "22115      1.0              0  ...   18    1  1.0  5.0  2.000000   1+2   \n",
       "27461      1.0              0  ...  125    2  5.0  4.0  2.507934   3+2   \n",
       "\n",
       "                 af12  af13  af14     price  \n",
       "7793   4199*1786*1479   808     1     658.0  \n",
       "22115  4633*1840*1530   404     2  109000.0  \n",
       "27461  5825*1926*1541   307     2     596.0  \n",
       "\n",
       "[3 rows x 33 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dp[x_dp['price'] > 300]  # 3个异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "76d231d2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:17.918264Z",
     "start_time": "2022-01-12T14:57:17.903267Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp.drop(x_dp[x_dp['price'] > 300].index, axis=0, inplace=True)  # price大于300的删除"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00796a78",
   "metadata": {},
   "source": [
    "对于还没分类的一些目前为数值型的分类变量，先看一下他们的散点图关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "13c61c1c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:30.254260Z",
     "start_time": "2022-01-12T14:57:17.920222Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kK6gnK155mun2hXjojVoA7tpYMu0e1JlGkyPI1546AcCPb12KL5wQPorGZTo8YVzqRIRMKCrx+pl2nj3WiiJAul6DNxhFq1ORbtRRmmEk22qgJMOUki89ESY6nl2o2mazRs8sF5ROb5gHtp3BFYzS7gnR6okAUOsECPPdf1Zy/eIcctP73PydvjA7znaBAmWZRtaWXbhaDsFonB89f4bqTj/pRi27qrr5zF8PEZXAEYzxvWdO8p7VRawuSU/x4tR2+XnmaCuQ8ET1ht41OYPsrummyG4k26Lnv3fWYNGp+fCm0hRpa4AVD+wkJiucbPWRbtRw72XzhmzjZf++nQZHCEFInO/uzeXUdQd45mgLtV1+tCqRNm+YFYU29tY6eM/KQjp9EfIkePF0F50OJ9kZfUbGsSYXP37hLLKi8MENxdy0PNWI+PXOWmSgzdPJ/d4oBf0STA/WOXnqcAsKiTyh/kYYwPv/ex+NzgC/e72GnV++DJupzyA81+Hjq48fJxqXuGtjKXesK07Z95XTHZxp82LSqfjoprKUCcfRRheffewIkqxwpNHNN989vJE4Gg3OEHnhOMdafQhAhzfEG1VdGLUqSjNN1Hb5ONrkRQH+eaKDudkNzMky0e2PkGM18NLJdnQaFTcuz2NOlpm/HWjEFYxRbDdy6+pCALYdT3jL0gwaPrqpdFyrYtOJLxzni38/ysoiG3dtLB3TPocanLiCMSRZ4WCDk7unUI540Te2EewRPCy9b9sFNXxCMYlAJE6zM0SXL0yR/Z0bjnPpj17Em+jauerhs/TP5HL4I3z6r4fxheJ8ZFMJH1ibeM794TjHm90szreOyUvcv27fRP7Xf9zTQG2XH1mBFneIm1cWTIvEuTskYQAiwMcf3s3DH9807mNctySX480e5mabLyoPYyCm0DsFv/d/9/DYpy6d1vbMNH67s5rKNi9atcgjuxv49BUVGDQqjFoVz59oIy4rbCzP4KVTbfxpTwNxZejjCIBJK1KSaeZr1y9k89zEExuMJhR0BUHgmkU5F4XRefE8HbO8Lfj7wSaikkwoJhMNR4bcpjcRvpd9tU7OtHr59fYqHnj+DH/cXX8BWprgRLMHtUokFJOIyTLzcy3E+ikXOwJRANyhVJna/u/7v36zuptWd5h9tU7+uKeO3TXdvHKmg8p276Bzx+W+HupAvXvYNroDieMrCrx2piPxWTCKokAoJvcYIAJxWaFoCHWgJn+qPGyrJ5xMym5zD5ZQ7b91fXfq/0qr6XNr6zWD11S6fOFEu+Iyza7U///hBhc1XX4ancHk7+iPJ5S41sGoRFRKbXODI4jUc73aPEPfVxMhcUSBUEyi2x/FEYjiC0v0Hzu6fGFCMYk0gxazXk2kR27XHYwhK+DtWX1LuSeCid/iC8eRlGFGohlItz/C4UYXf93fiKfnN4xGhllHKCoRjsuopnhCFpwahfcxE4nLRCWZtIsor2IiNHoGy7v3sqfWQZMziDsU5aVTfc/597ed5pevVXH/0ycndM7Xz3aijONZWlNiQy0KqEWBW1bms2oIr/aF5kjL0DWNRiMvzcC1i3NntPDD+XKy1TfdTZhRSLLC8SYPvkgcfyTOmtJ00gwarl6UQ7HdmJw/uIJRunxRpBEeDQWISQoxSabd2ycNfrLFS21XgJpOP6daB89RZhJ7ax38/WATTc7giNtNW88sCIIOaATOAC2Konxwutoyy4VBkhWaXEHy0gzkWvX84gMr2fzT7clJtABkmTVsmd9XWHJHZSctriC+SAxBALUo0upOPJQOf4SYJCfVS6YiFjTfZmBejoX8ND3vW11ISYaJ/3dlBT9/rRqVAA/euZSApGbjgErsSwvS8PZMcgvTDXT5ImRZdBSmG2j3hLGbtAQi8WSYVppBQzQuc6rVQ0mGEbtJx8c3l/L7t+oxaEV++N7lyWN7QjFCUSkZPvWT9y3l//3tGFq1yJrSDDq9YZYUpOEJxSjJMOAPx1mYl8a8XAtphkS4ma7nWtn0alaXpCpqXb0gh8o2L/6IxO1riwZdk0KbnmZ3GJNWZFVp6kRhVXE6924pxxeOc9WinEH73ra2iMcPNTM328LCAW70HIsWm0FDXFYoyxxsnF2xIIeD9U6KM4yDwttuXlnAsWY3bZ4wX7pmsEdMkiReOdPF4nzLqCvw/Z0uH1xXyLryDB7d34SsKCwrSOOS8gz+541aOv1RDGqBpflWbl6ej6RAYbqRqCQnti20oRIFbliaR1WHL0WR5prFuRxrclORbZ4WaePzIRqX0apUqMYYY2/Wa8iyaAhHFSqmuHZO/Y/flfQAZJsu7HXVqUXMWjU5VjXyOzz/4JXPb+HqX7wx5HfrSu1kW/X4w3EuX9DnHW92+KjvDhKOx4jH5VHzW+5ZV8jv9zcDsCxfy9FGN/NyLGP21ly5MJd5ORYkWaE0c2YYC3/+xJbpbsKM5ZFPbJjuJswoJFlBIypY9SrKMs1cs7hv3lRkN3JJRSbuYJRNFZkU2Q10eUMcaXYjKyDLCUNHLYJBI2I36SjPNrO0wMa7l+Ylj5OXpkclCgg9r2cqnlCMPTUOILGwPBLTuRyVBvxTUZR7prENs0wRp1o9VLb5WF5kS050VKLAquJ0bAYtC/Os5NsN1I4QkvDH3bX87OUqAO5YX8SHNpTQ7Y/y4U0lNDgCPHWkhRPNHgrTDSwrtCVDh86H3dXddPjCXFKRSbZFT5HdyN2byxLu356J9mevns9nrx650rlGJbJ1fjZtnhCP7W9CkhVuWJrHlrlZLMlPw6xX0+2LoBIFctP0bJyTyY9fOMORRjfpRi0/uXUZ9797MR/dXM7jB5v5y74G3r0sjwyTjr/sayAmKWydn8XK4nSuW5KP3aTnrepuLHo1VoMGjUrk0rlZPLKnHlcwRocvzNp+ylklmSa+8/7lQxYOdASjqEQRs06gzRseFGpy3ZJcDje6yU3ToxmQ6C8IAltHEEhYWZyOoghkW3Q9q7J9k8PL5mfzoU0BnP4YH7ukdNC+WRYd1/frkAfyrRuHD2n7wj+Oc7DeiUGj5m+f3DCiSpNWJWLUiJh1Kr55w2JafGGyzJ00OIK4QzECEYnb1xVzus1LeaaZj28pS1EaHEhFtnnQZD/fZiB/GkJpzpcMk5YrF+RQmmnCOMDwPNzooq4rwLoye4oYSVO3nxZ3BFmBXWe7+NyVQ4dpThYXOpenl0BEwhmK4YnEkWd+bc0ppSjDxPtXF9LmCXHXxhK+8Wrfd9lWPX/46Fq8oThF/ZKwjzT5CMYknMHYmBL6v/ne5Xzzvcs53Ohi59kuQjGJnWc7WVZoG7O4zEwIQey/yGLQDL/dOxGx3+KBXv3O9p4ORK0SqXGE8IXjeEJujjQ6WVncN8av6zfeLymw8T8fW8frlZ1Y9Go2V2Thj8RJN2pGFL0Yav4zEzFqVaQbNbiCsVHH1en8FVZg1l95kfC+3+2myRXkc1dWkGM18MvXzlGYbqTbH0mZ8N26qhB3KIZ9DDHbp1t9yXCFJkeIj2+ew+6abhodIbTqhFRoMCr1hB6df0hTpzfMvjonAAIOblmZyGUZTQHlSKOLxw81szg/jTvXFyPLCjuruvCH4+RYdcmwK4c/Alh4+mgLNZ1+7txQzFevW8Ab1d08e6yVBkfCLdvmCfGPQ41ctTCX3TXdnG7zUGQ30u1PGCMxqfd4feFF68rsVGSbMWpVybjbuKwkQ6q6fanXp9Mb5j9fPUux3cQjd69LiQNvcQV57lgLMUmhON3AgtxUj0xhuhFBEDBoVEgo4+pEqjr9NDgCeMMxYpKSkuwtiiKf2DK0iMX5Ut8dIBiViMRlWt2hEY2eqCQTjMlEJQVvJE6HN0w4JuEKRjnd6sWgVbGqOJ3F+Wncvbks6T2DhBfk9bOdyLLC5QuyL4oY6P64glEe3d9AYbqBuzeXJT8PRuPsPNuVeB2T+NCGkuR3BxsTtahEAdp9F2/FeVlRkhPYum4/dvPUynPPZLyhGM8dayESV9AMmFQpisLxFg/OQBSzXk16j9JhTJYRBZAV8AeimE1afOEYO88l8ukum5edohRZ1e7jzof3AvDrO1ZwtMlDhzfCq2c6WJhnHbcU/3TRL4qZE80+SrPOL5H8YkLuF654qs3NomksuDvTkBUFqd8zU9sVSDF6BnKw3kltVwBIFAOuyB684NnLkUYX9Y4Aa0vtFPYLiVcUhTeru3EGolw6Nyv57PbneLObmi4/q4vtaNUCv3qtmg5vmLnZZtaV29k6L3vSclgPNbhodAZYX5bBnetL8IVjo8rOT6fRYwCuFgRhN/Cwoij/2/9LQRDuBe4FKC4uHmL3WWYKf9pTx9EmFwICv3ytmkV5VnyhOMcDHq4eUBVdrRLJHGMthC9fN596R4CYpPC1G+bzZnUX9d1BarsCfGBNEcsKbWSadZj16iFVxsaKoih0+SJoVCImnYpARBpXsa8/7m6gzROiutPP5fOzCEQljvYU5tOpRVYU24jGZVYWp3Ouw8eLJ9sB+POeBu7ZXM6uc10YNCqWF6XR7gnT6g7hDcV55lgr8XiiwKmsKKwosqFTi6wtteMNx1hfntrBDZRJ1qpFrl6UQ01XgFXFtpTvAtE4nmCM0xEvoUgMi7Hvf7Kv1kmXL4KiwFu13dy6JjXE7apFORxudDMnyzRuxRUBEEQBQQCFC5fLsr7MjjsUJcusG7a2Ui+946wsJ2Kc3cEo3nCcqCQTl2VK7BYqss0U2Y0pBg/A6TZvsjhmhlmXstp2MeANxzHJUOcIsb+2m3XlidBInTpRnd4ZiJI74Nl54KbFHGty44/G+eEtS6a8jd3+MM5AbEgv5lSSadaRptewMN/K6iHUB99J/Nf2c4R7sqZ3VXfTv3dudoU4WJ/IXdGoRG7o8d7euqqAl052sGGOHXNPX3awwUVVRyJvsDDdmPI//fLjx5I5lT98/iyfuLSc6k4/2Rb9lBg8p1s95NsMUyqFHozGRt/oHYrHP3tt+qMWBe7YUMrfDzUxN9vMu5fnj7h9Yk7jQatOhLMNRyASTwhHkfBe39WzgCXJCseb3eyvcyIKAlqVOCjyIhyT2F7ZiaJAuyeMLxxnf70DZyBKoyNAJC5TkmFiziTknnnDMXad6+o5r8wd64rHVGdr2oweRVFOAIsFQTABOwVBeFpRFGe/7x8CHgJYs2bN2yfT9x1CXJL537fqeLOqiwZnMLFapSjkp+kpsBnwR+JkmXXctCIRclbd6eNUq5clBWljvuEzzXr+9sk+JZsmV5j67iAmXWKCNVTOyHjp8IT4zes1BKJxFuencce6YsIxmVZ3kG8/e4plBVZuXT04r6U/JRkG2jwhbAYNaUYNeo0KrVokGpfJtxlSQi2ye+SW/ZE4JRkmDtQ7OdPmRa9RcfclpRTYDKhEEUEQKE430OWPUJZpYn2ZPek16FVWGQuL89NYPIQ0tCgIBKMSNqM4KEzJalAnBBCURKjXQArTjSmrP+NhXo6FmKSQbtRcMIlKgEvmZmHQqrHo1aNKava6+0VRYF9NJ7KgSuZgLS2wsarExo39BhhJVthV1UUkJjEn24woCCgoZFl0dPki7Kl1UGAzTLlU84Wgdy4pCjCv33OsEgVuX1eEJxgjy5I68KhVIoUZRjzB6KQVbo1JMq5glEyTLmXVsMUd5GtPniASk3nvqgJuW3vhFsy0apHsND1lGdMfMjXdrCmx88jeJiCx8BOTZJ491kpphpGyTBMqQcAfiZFj7bsf1pTaaXGHWd9PnTPHkjCgNSph0KLO8qI0TvQsMCwvSuOGpXk4/JEhV5/Pl9/uqGbn2S4sejX/edsKLPqpiUNbnDczcotmIitKbNPdhBlHhz9CjlXPrauLkuNpNC4nF/j6h64tzk8jL82ATi2OGKqm6ymg7g7GUhZ/nz/RxqlWD3VdARbkWYcM6daqROwmLfXdAWq7Ij3zQnoWlNVYDOoxRfmMBYNGhdWgwRtK7UdGYzqFDNSKosSBIBAiVRRqlhlKg8PLLb/ZQzgqo1MpSIjEZYXSDAM6tYqnPr2ZaFymst1LRbY5ueL24sl2YpJCqzvMv26dWMe+aU4mFVlmLHrNoIlruyfMgXonpRmmlITxkXD6w9z4X2/hDkbJMGtxBaMsyreyoTyDB7adoc0TorLNyyUVmVR3BvBHYmyemzUo3O1zV87l6kU+itINGLVqjFr4yKZSwjEJlSCw7Xgb2VYda0vt2Ixa/v19y2n3hliUn8bDb9SyvMiGJCs0OIO0ukIYNCquXJjN4vw0onEZXzg2Ys5IL75wjDerukkzaNg4J2PEWF1JVtCoBCI9x7eZ+q5nJCYjyzKyAtHY8CpMQyHLCq9VduALx7luSe6g+knZFi2vekIU2w2TuhqrKAp7ah14gjEumZuJdcCk5NK5mSzoEXIYzdhSCQmPlIhCVXcIRyBKulFDgS2DdWV2rliQjaIovFXtwB+J4Q3Fef5EKw5/lKVFaXz5mvmAQLpJy+OHmmlyBqnp9FOeaZqSCdmFZEGulevWFHLb2mJsltR78t2/eoN6R5BbluXx77etTH7+4Bu1HGtMrOz/YNsZHrt343m1QVEU/n6wiU5vhPm5lqSnABJhsJFYYiip7fIPd4gpodMXweQK8pw7xN2by5gzxaINM5n+uTo5Vh0N4Tg1nX5qu/zcsbaYB3dWE4nLKLLM6pKEV+zBHTWEYhL13QHuXFuMVqtiUb6VHKsOrVocZGh89+alzO/x/Ny5oRRgTP3kRKjvToQF+cJxun2RKTN6Wj1Rpt4X+vak2RViTdno271T8IRiPH+shbAEX3n8ONlmNZsqcnjsQCMOf5QlBWlcPWBheCxFs9UqkTvWFfcYPX3GRIc3jE6toiLbwh3ripNCSv0RRYHb1hbxVpUjEf0jCFyxIIt5ORbSjBrspqFr7E0EjUrkg+uL8YRiZFveBkYPsFEQhN5CzX9WFMU9jW2ZZQxc+tPtNDr75AyjEhh1AplmDUsLbMlVVa1aZFmhLWXfTLOONk+YTPPQD90f3qpjV1U3712VP2JxyeEGte2VnXR4w9R0+SnPMo0p6e4bT5+i258I43L4o9gMWvbWOlhakEa+TU+bJ0SaQYPDH+FAvZPjzW6eOtLC129YSEmGiXBM4s2qbjRqkc0VmSmTeLNOjVmn5v/equNAnZMMs5YSu5Fsqx67WYu95zpcviCbo41uJEVhX62DDm+EtaV2yrMSBqNBq0ox8No9YQ41uCjLNJFl0XGg3kmBzUA4JvHHPfXIskJppok6R4DCdCObKzIRgG0nWjnR4uWaRTmsKbUjCgKSDHqNMKgTOtLkplcR+kz7+CaO+2od3P/UCWKSQrMryGcHJK3/ZkctdV1+zrT5uHx+TvI6AMTjcT7558M4AzEeeM+SIYuXDkdDd4AHd9QQiEh4QzFuG1DjRxCEMYcsxuVE4F1Uhjequsi06DjW5EYUBa5flIVaJVLb5edAvZNmV5C3qrtxBqOggDcS48al+WysSHjjMs1ampwJ7+TFUIRUJQr86H3LB33+2uk2qjoTE8PHj7SmGD3lNhW+SMJ4DkbPX1M6LifCUYGkkmMv68rS2TI3kw5vhNsvoJcHEt7v3t85nvDYi5G/729Mvq53hND09I0WvYb7njhGsMcwffitBu6/cUnPd2qcgShZFh3afs/KUN7BA/VOzrZ7aXWH0WtUNDmDo4atng8f3ljKo/sbqcg2UzaFstBVHV6uWTpymNJU8avXzvHyqQ5uXpHPJy6dmtzK86GybWZLJl9odGoRf8+apALc88fD/OjWZclc3zZPaPidR0GvUZGbljpeXbEgmyONbubnWoY0ePrapWLz3ExisowkK1w+P3vKxj69RjXuvNnpDG97Azi/Jb9ZLggOf4TVP3h10OelGUbet7qI964uJMeqZ8E3niccV1iSZ+afn7uMxw818cKJdt69LI/3riqkyx9BkSVePtnGNUvy2F3dhV6t8OvXazjbHkQQ4L9eCw0yev6yr4FGR5C7NpYMWWcGIMOspcMbxqxTj6lK9huVbeyt7U4mkVZkGSmyJ6SQu/0RPn5JKVcuzOGLjx3iXb9+C5NGwKzXkGHW8dCuWj6+uYxWd4gTLYkk7UyzlkV5VpodQX7+yhkkBP5lawXVnX66/RGqOn0gSeTZzbx/TRGioOAJxnAGohi1KpyBKIXpRmxGDe9alodGJdDkDJJl1tLmCaMSBaJxmb11iXybF0+0kmHSkmbScaDns7quAB3eEDVdfi6pyORYk5uqDh/zciw8uLMWTzDK8SY3v7trNYqiEJMVZFkmLqd2BJvmZPDc8TYAlg3hNfvWU0f5+6FWlhVY+fu/bk757uVT7Tj8iRpBr5zqGGT01HT6qO0OYlALCEJq1Opvd9SwvbILBbj/qRM89enUY0Oivo1Jpx4k81zd5ae604ekwL46xyCjx+GPsL/OSWG6cVRPYP9W1XX6qO70E+nJT/jP16r5c3kWerVIuyfEq6c78IT7JvId7jD9HWyXzUusctl6wh6T51AU3MEYVoPmbZNwPRL9C9QO/D3ff74y+fp48+CJS+l920jXwJHvj011TaMSuWJBNuc6/INy1URR5DNXzB1HyyeP/vfNs0eauXOMxVsnmxZniL/ur+czl5ViMEyPQuCqEhuPHmwBQK8RMRk0fGBtERkmLX/ZU5vcrvearfzuNnQyxAU1VoMaWVYQRYFQVOLN6m5MWhUb52TQ6grz32/U4A2E6QzEMWlVLMpPo8MbTho9zxxt4WSLh/esKmBRXuqzXtcZ4OE3q/ncFRVk2/rCEE+3ecix6IcNv1xSkMYD71k67O/1hmNoVeJ5C5eExulZn0z+9806JFnhdztqZqTRc6E9tzMdvUZFgL5nKK7Af75cydryTJYVpLF13vAqqhOhPMs85jpQWrXItf0ktGcSM1eDbpYZw1AGz+1rC/jxrSuS7//tz/uTiasn2/wcqXfwq9eqiUsSe2q6eXR/I1+9bj4f/cNBIjEJrfoYkiQR6rfwm2ZQkzHA/XqyxcOzR1uBRILdpfOyyDBrMWjUNDqDrC5JJ92gocHhp9UdZGVROg2O4Kj1QO5+5DCxfgGVZ9oDbCoPYtVb+erjxwnHZTaWWKjvKaAZiCmoVDL+cIzDDS7u6/QzJ8tEXFbo9ke4ZlEOzxxt4Uv/OEZPbUrqHUE+tKGE50+04QrGqO4MUJJh5FyHl5rOADXdfrQqkSsX5KBRiywpSKM800Btp5dfvHqWs+3+xARSUahzBDHrVFw+P4fTbW7OtCUGgLk5JnKtBppdQZpcQWISuENxwjEZQRBo94b5w+46un1RBAEMWjVnO3zEeqw9b0TGGwqj1/Zdr6cP9a3SvnSyjZ8MWNl/ZF9iMrO/wcOxBgfLS/pi8GVZShZBc4cHF6+s7U4o1IXiCicbPWzpJ3Tx0vHWZAd+pMkzaN83qro4WO8iw6zlznXFKYpzVr2GYFQmJslDGhHbKzup6fJzus1Lsd1ImnFs4SmBOPSfzh5vchOKxHhg2xm2n+0gFEs13MKSwrEmNxvmJDw9giAMKaH5yukOTrV6yUvTc9vaohFDESERNri31kE4LrFpTua4JlfhmISsKINCDSeTBbk2vn7dAp473sI335UqHd41woJjb00dVyzxeqxy08sKbYO8yTOJHZUt02b0bP7pdhTgoTfqqHrghmlpw+9eP5N8HYrJmCBZP6drgIJf7z2QIM6BejfRqIRer2Z/vZOTPQtL2VYdV/98V5+hVJRGvSOAJxznzvWJvMsOb5hH9zeiKOAMRPnZB1aknOvyn+8A4NH9zclyCb945Sz/ONSMQaPi4Y+sGXfNnpMtHl453YFRq+LO9cXnFfr2Xzvr+dL1w0vvTyVmnRpPKLEQMxN5uXLk+ivvNCIxaZAUUKs3yulWD8V2I8UZU+f5fDsza/TMMiKX/nT7oM+W55spzUhVRjrSkFpJ+j0P7sWkFRAQCcdlzrR5uf/pk0R6VrICEYmBHs/CdAMb52TS6Q0nw9gyTNqkKEAwKnGq1UtMkonEJcw6De5glEyzltfOdNHuCdHiDtEdiPKprcOvVP1uR1WKwQOJae2rZ52oRDeyohCNy9Q6Uw2wonQD5Vkmml2JWVy7J0yjM4g/EueXr1UxN8ecNHggEXN77eI8vv/PMwg95whE4sQkhdpuP+GYTCgqc6jRxbLCRP7Od14+iz8cRxASdWncwRg6TSIROBwDZyCCLxxPdnYtrhDlmWasBg29MiCKklAB0qrVVLZ5CcdkJElGpRZJN2oGJRLuPNPB+zf0DfRH+1W+dodHXnlUSJ2s729wJ193+wYbPf3pDqZOfpo8I8uONzl7i9JGCcYkrP2MnqgkU5JhICYpQyrTdHgjHGl0Y9VrOJ9aoIGYTL0zSFWXPxkC2B+BxH0xGk0991CbJ0xMUtCqRzZ6znX6knLqerWKTRVjE7Po9IX5x8FmJFnhlhUFUzoQ3rt1DveO8Ny9k/CHpmdVusUZSvYNsZFKsE8xtc7hU3QvnZfDtpMdI+4fjMbQ69Wk9yxOqEQBbzCeMsmTFIVoTKLFFeQPuxv46nULMGnVGLUJBc6BKqGd7kDydf/WnWhJeB9DMYkTzZ5xGz3NrmBPmyUc/uiU5ftMNf/4l008f6KNG5dNT3jdLOOjtjtA1hCfq0RxkILmLH1MaPgXBOEVQRBeHulvshs6y4VFURTu/eOBlBwegDwN6HVaKtsTE+MP/s9ervn5TuxmI4YB8zZJUlhbZsOgSdxmSwttLM63YtKp2VyRQba1bwVcLcBVC3OxGlJFCvJsBn743qV84ep53LoqEfamU4tJqeA0g4Zsqx5REHoKamqw6NUjhgz9tUdVqBcB0KsF7CYderVAIBLHbtRSkWPhC1dWkGHWsKrIikYtct2SfN6zqpACm4F3L88lEpeJSwq1XQm56tKMxG/SqwV+d+cqrAYNd64rxqxTkWPV8snLyllWaKMw3YhGJWDSqcgy6xAFgf11DvyROCScO5j1alYVpzMvy4xZp8agVfPRS0r4wS1L0KtF1AJcPi+LVk+INk+YxXlW0gxqMs1a1pbZKbEbmZdtwWZQo9eqWJRnZWmhbVDse74ttYP88Ma+Iq/LhlATumZRFqIAZXYDK0pSpXmv7FdhvWSICfaKgoSxbNapuHFFajHZ/7t7TfL1VfMHT+o3V2SSl6Znfbl9kFDBurIMLqnIYnFBGu8fokhthlnDglwLc7JN5zUZLEjTYjdqee+KAnQ9/4P+ZJq13Lm+ZOid+3HZvExy0/RcOi9rTOGYVr0mGTbXuxIbiMSJD2V59aPNHSYaT8RWN7uDo55nOllb9PacLA6F1XBh5bJ7KbAbyLHqEIB5OdMnpPDUbakT50Akzqf+cpg/vFXHb+5aw8bSNLTq4au8B2MJeeJlhTY+sLaID64vZllRGj1DCXoVzM0yJ729xT3hlWa9mh+9dxmfu3IuX7wmtYB0ts1Emj4xtuRY+hZ+Pr65jDybgRVF6Vy7aPwhOWtK7RSkG1icb6X4PPOKluRNX8HiPJuBe7aUkz1CvsZ0Umy7ePqHyWCoGc5tq/N54Jal3Ll+tszLcEzU0/ODfq9vBzTAo4AOuAc4en7NmmU6iMViHG/xsazAwo9eqOTlM53J7zIMKpYWpxONyWRbDXxgTSE/ev40e+scKEpisnfX5jJePd1OnSNhKKnVKmwGHb+6fSXN7iAf2VQ+7LkVRaHRGcRm0A5aKStKN1KUbkRRFIrsJqwGNWqVSJcvQrHdiEoU+O7Ni3H4o+Sm6cm16ocNFzra6CYSl5IdxuriNFaVZCAIiSTSI10BDFoVc7JN3L62mCK7kU1zs/jdzhqyzTo8oRgfWF3ITcvzsRm1nGzx8frZTgptBv66r5H3rS5KGkW93HfDQu67YSEOf4RH9jQA8KnLK4hJMrWdfiJxmZMtXhblW8ky6wjFJN61JId7Lq0g36bnXLuPJw63oFOLGLUa1pTaOf6da/n9m7Xs7RE/UBSFTLOWZ/5tS0IJzhEgEpfIsRj41fZzdPsjxOIKVy/KGZRUOGdA3Hu60YC6x/Aqzhw8eavpDGDUqvBHJFz+KOn9xAiWF9vJs7YSlWSuGWIC8b41xSwq8GEzaojLqcVJM0wGPrV1DpKssGFOxqB9izOMFGcM3Zlr1SJfuW7BkN8BbK7IAgQKbIZRFdTKMk0UZ5uo6gwMCh8IxhMrzJvnZfHE4SZaPWHi0YThoVUJ/OWeDczNHX3CW5FtGbE43EDybQY+uL6EqCRTYDNwrMnN9spO0gwa7lxfPGy42/xcCw3OILG4zNJpKuynV8FwDsOxhrONh/94+SzNriBfvHoeRfbpkY9eWTb4/r1Q7Pv6VdN27l5WrlzJl91mzrb7+PTlFWz4a5hjTS6ON7upyDGh1mhYWWQn36ZjQY6Fsx0BnjzSktxfr+6bmvTvSz++pZx9dU5MOjXZaXoyTFoyzTpu7Kfgl2PVDyskcew71w36bGNFJo+P0XM6FJlmHR9YM3JZg7GyvnSotftZAFaVTN8zNRPJGCAKtbrQwgfWlfBWdRevnenglpUFzBvDWPROY0JGj6IoO3tfC4LwK0VRlvd7/zJwEPj++TdvlgvJuh+9jjsYI8OsYWVhWjIkSxRApVZT2eZDJQp8aGMpmyoyqXf4AQFZUej0RfmfN+owaPqMDb1GJMeq58ohJr9t7lCiqFaWhRuW5SEIAiWj1LcQBCElPKe/6ti8HAuMUrZHURQMmkRdGpus9BTKFKls95Jt1eMNxbAY1PjDcfZVd3PF8dcx6lR8autcFuVZaXQGaXEH+cyjR/BH4nxkUyl3by7DEYiyu8aBRhSIRmTuOrSXuTkWfnvnCtRqNf842MQbVV3curqQk60ejjS6mJ9tYUVxOvvqnLgCUWKxOMdb3Amltc9toiwzjVZXiPufPIFeo0om2AajEtsrO8i26AnHZLIsOo40uolJMuc6A7x8uh1fKM7fDzZx7eIcFuRZ2XmuiwyTlptXFrKhfPDAkaZLNTJP1bfSk57F0frBYSitnhChmEIwKiEpqWZBtkWP3awjLslDaue/eqaT3TXdZJp1fO7K1IRzfzjG/71VR0ySkWSJy+dPXiJmkd3IHevGtvpV1x0g3BkY5luZSExmb40Dd0hCrxYJxRLy3nFJ4TOPHuT7tyxj/YDrfKzJjTsUY12pfUglG0lW2N8TvrauzD6kp7J//Zt6R6J9nlAMVzBKXtrQK8R6jYqbRilaN9Vo1QLhSQ616p8H0t9wevFkO08dbgbgRy+c5bcfXDWp5x0r05kX8clHDvBWjYP3rSrgOzcPn3w/lRysc/DvL50D4Nljbcn7WacWERF4o6ovP2PL3Bhfu35BitFjtwy+nyVZQatWsa7Mzpk2LwfrXLR7w7hDMR58o44vDfDsTAUvnGjjhZNt3LwinysXTn6itj8yckjwO5nYJCg/XkyoRCElTPNQs49bf7cn+X7biVae/9ylg5Rsjza52XG2g80VWawZpojy0UYXB3tUYjfPzbygNfWmmvOIbk+iFQShv3p6NvDOLkf9NsMXjvHTF87gCsZQAIc/xp3rilhdasNmULOkwAooxCQFURCw6lTc/t97ePJwK5/YXJpyrP6J3bG4wn03LBzynA/uqmV3tYM/7qmn5gKosiiKwmMHmvjn8TbuvqSU960soDzLSCASRyUIdPkiFKabKM8ykW7U0OaPEZPBE5L4zfZqGhwBrHoNzc4gzkBCnexoo4vCdCOZJh25Vh16jciu2i6a3SF2nO3kl9trcPgj/Oq1Kg41uPjR85W8WdWFJxBLqLB5g/jDcfyROO3+KKGYTDAm88GHDgDw4xfPsKfWwetnO8kwa7huSS7PHWtl+5lOtp/pQKNKyDCXZSYkuiVZ4dG9DTyw7TSnWz38ens1X3viOF2+CGfafFRkDh16caQxVTTg+bN9/49m3+Dwqd7/sawkirv2x6xXs7wwjcUFVkoyBofXvFndRVRSaPWEOdLkTPnut69XE4ol1OSeOtw6aF93MMrrZzup7pw+FR9PMI5GJfBaZScmnQqzXsPCnjAiGajqDPKtZ04S75fc1eQMsr2yk8MNLnbXDJ2Me6LFw95aB3trHcnE7ZFYW2on26pjcb41WcBxprIgt8/DpBvn2BmOSSgDDOvP/eWtYbfPtiRCRQHSjdOXsppnnZ4kYrcvzEunO/FHJP64p3H0HaaI9/333pT3RXYj/3LZHH77wVXc+fD+lO+icZmjTZ7kZEQAwuHBE1yVKLB5biYFNgNLC9NQiwJqVeJ/nTOOOh3jJSbJyTDSn7xU2dOXn01+X9/t58O/38f9T54473P97XD7hPcd6lm5mNh2ZlbIoD/yKP/qRmeI54+njqORuMRvXq9mT42T3+2sGbKEQJMzyBOHW3jldAdPH23hYL1r0DZvZyZjVPgi8IYgCHuBOLAV+O4kHHeWC8S3njzOkZY+KVmLXs1lC3K5fFE+x5vdPPxGHauL05mTaWZOtplH9jZwuKfYoHEE/fUsi46d57p6BABSJ8BpejWBSOKB01wAyd7abj/PHmtFUBQ2zsnk/hsX8/SRZnae68Kq1+APx8izGdmanc32yg5quvvyHxQUJFkm3aghGpOwm7ToNSpuXpHIMbptXRGOQBSrQc3hBhfnOnwoikKWSY1OJaBTi4RiEsFoHEVRkACVmEi+X1aUxqEGF6FoHH9PmJStJ/wqvUdwQBAE5uVYOdfho8sfIRSVKM4wceuqAl481c6iPAvd/ghalYhZr+kpOqqg7amC7AxE0ahFsodYPQWoyEh1k6tFBgk9pHwvkPQEpQ8QDTBqVWRb9SgKQ3o0VIJAvEf+IGtAmNmaknSeP5XwLA2neNbsCnG8ycPHt5SNqRbTZJPwFGowalXkWPUU2Y3oNQJVXQGiUqK+j06jQuy3nGTQqhAFYUQFtf7PkWkMlkFvuNtMwu/3c6TFz5b5qSvg/UUaxvOk769z8lZ1N3lpet6/pijpLVhcpuWZYeaXq0rS+emty6h3BvjAELldFwrrGNUBJ5v+kcGjCAJOKSYRAv36kN4IgaEoyTBy1cJsdpzNZk+tkyX5VozDXL+1pXZMWhWfffQI4ZjE/GwLH1hXNGJtt/OhyRnk6SMtaNUit60twqBREYvLKc/rVx4/zpk2L0caXSwuSDuvfIrcCab0vH62k6ONbsoyTdyycmquxXQzq0WWSrpRy0jmiEY12OOsEgRMWhXeUAyDRoVaHOz3MGhV6HryTLUq8aKoL9ef8541KIryvCAIS0nU3NEDX1EUZfqWmGYZF/3DRPLSdORYdWgEgc88eoQfvWcpywpt/OqOlfgjcY40usiy6snqCbWKSQrIcW5flcdjh9uwaEAUwReBzeVW3r2qlMMNLo42uvnEpWXJCd+ZNi8ldiNFdgOZJh01XQGKRwltO19+8kIlJ5tdmHRqbCYttV1+tCoVKlHEolcjCglltCyLnc9dOY+1xWk8+EY9OpXAwnwry4vSSTNoCMckcm0GNpRnJItnzsux8IvbV3Cs0UV1pw+9WsQfkfj3l6u4cmEev7pzJU8fbqHbH+Zki0C9I0S6UcOqkgzuWFdEZbuP2i4///nKOTyhOJfNzSIel/n05WWUZpgozzSyoTyDfx5rZV62iaik8J6V+WSYdczJMtPpjbAw34pRLbKq2MbBeieiIPDRS4q5fmEOjx5sZl6ulRZPCLtZOyinxWpJvfbfvXkxX3/qFAC3rxk8cfzQxhIeP9TEsgIbOQOSXvPSDFh0Krr90WS19P7MyzVyqiWA1aAic4ARtm5OBiatiqgkc9WiwTH2vZ2vRi2Mu7ZNdYePh96oZWWxjTvWTcxYEIDb1hZiN2n53i2L+fHzlQgoGNQqskxaRBXkpRn57s2LEfsNJplmHatKbLS7w6woStwzzkCUo00u5uVYKEw3Mi/Hgn6VCkFgSossThXtLj8bfpKIetaIUPXDvpCz/rVPxjMRr+pMiKW0ecL4I/GkeMm9m9byk2e3IQGF1sH7bazIZCMTz9GYDOTIxAsDng8arY4r52dyvMWTIipyoTn1w3clx5YvXV7MH/pVPfj39y7hy0+eTL7/aY8k/gPvWcaZNi8V2WbC0Tj6IRYIWtwhXj3dTrMrRCwu0+wO81aNgxUFFgozhrgZzpO67gBxWSEelWh2hfjdB1fx6plOrl/Sl0Nk0SfaKQgCmZbRK96PxBevnZhcdXWHP9nemCQPqmN2MfD5ayumuwkzCpUopCxA9md9sYWPbqng6gGpBWqVyDfevYhD9S5WltiGFNHJNOu499Jyarr8ZFt0zBlH7unbgclaKp0PWAAVsFkQBBRF+eskHXuWKWJeSn0E+Nr1CznU6KayzYvDH6Wq05+M+XztTAe1XQEEwcUXr53PqRYXh5q87KpxY9R4qPvRDVR8/flkjZYPbighJCUeKI1aQBTgSKOLbn+EE80egjEJtShiN+vQaaa2g95R2cFrZzqRFAjFopRmmNhxtgtPKIZFp0ajEnizykGD089zx9v4nw+t4c6N5dyxoYyXT3fwemUH/nCcZlcIbyiGzSijH9DmYDjOxx85iDccSxay9Eck/ri7jmuX5nO40U2nL0xcUrDpNbhDMf7jpbP8ZV8Dd28uwxWIoVGpkJU4L5xs47XKDhqdIUQxUeCyxR3idztricsKn7y0jKyekKb1ZXZOtbip6wpgN2l5cFcN7d5EXPhPXqziJy9WUWY3EIlDmjFEsys0aCWyyRFibl7fitB3nj6VfP3YwWZ+PKBOz/EWD6Io0uwOEYzEsfRbTXqjqouXerw1f9nXwCcvS5UwbnZGEtLdURlXIExaP/nsfx5rIxRN1B7YdryDz12VGhp5zaJcCmweSjNM4y4C+K1nT9HgCLC7xsHaEjsVQxhko6EA//NmIwoqFuZb2FvrICbJuENxRCGRsP+rO1azcIAwRIs7lAwR2Fvr5PIF2fzqtSrOtHnJNGv55e0rUavESZeTdgainG71UpZlSkkGnwqeOdaWfD3QS/jMsb6QndA4wvLXldp5o6qbkgxj0uDppWYKBBAmk689eoxXvn3hPU0qUeCeS+dQ2eZjdUn6BT9/f/rnWv3h/r7Pv9bP4OnPf22vYn+dg0BUwmbQcPu6kpS+SpYVfvDPUxysdxGV5GQ+g6TAlT9/g7MPTOyeCMckjjS6STdpWJCbajgtKUij0RlEpxapyDaj16j4+Ja+qAVJkvjuTQv56hMnmZttGVK8ZTz88OlTvH9D6bj321CewRvVnSwvTL8oDR6Af3+pmnsvn/q8rbcL7mBsSIMHwBNVKEg3Ig6xOJhp1nHtkpHv02yrPlk25GLjvI0eQRD+AqwF9gOxno8VYNbomeEMTJm8aUUBuWl6OjwhctMMKcX/et2dalFALQpUd/UlegdjClWdfvrnKv/boydYVWbnqvlZvHdNMTVdAXac7SIUlfCGY+RY9Vy+IJvF+WnMHaWQ6PnS6Awk2ybLCXWfhbkW3qpxANDujdDhCxOTICrFufdPB/jIhlLy7UbaPWEc/hh6jZp2TwhnIEqTEz62qTR5/F+8co7njrXiCkaRFZICEAAnWtyoVCId3jAd3jBWvZqolMiPisYl2t0h/rqviasXZaOg9CTxK3R4I8RlGUVKhFg8frgFTyiGoigcbnTx4Y2JiuV1XX5ePNVOly+CJCvEhwj0rXOGKHL4WGm0D7my4/QGIK9vsI+OEitc3enHF44TikoEo9EUo0cA2r1hZDkRPzwQT0+NoZikEI2lzoBDsXhyIuOPDN735y+f5Z8n2si3Gfjzx9ahHYfb3dBjJKlE4byN7FdOtRKTcwnFJEI97ZSVhLLbx/5wgCf+ZRNzss3JqvIalYDQo4bXe/27/YmaRJ5Qom7TVOSJbjvRRrcvwrFmN5+8tDylmOtk88mtc/nJS+eQFbBPUi7N3BwLcydgnM4EqqbH0QPApjmZbJozvZ6ukRjO7n3hZBsOXxQJaCTE6WdOpBg9kUic1892EYvLg/IZzieU782qbk705NGlG7Upym92k5a7NgztGT7a6OJL/zhGtz9CjlVPXPZxpNHFyuKJG5sTzZ742ctnOdTgpMhu5PnPXTrh889kYqNv8o5ipGCH6k4/BxtcM7qA83QxGaPTZUCFoiijV+SbZcYQjAw99Kwry2DdEHKrVy7MochuJMuiw6RT880b5vGlJxNVt7ViIvZzXraRc52JXJioklBo++nL53jozXq+c9MiIBGitKkig2yLnvm5lnGHKU0ER6DPvLMZNXykx2DJsurp8IbZW+tgeZGNt6q7ERQIRSV2VHWxOD+NHGuinSuKbDy6v4F2b5gMs456R5CSniJ2hxtdiGLCMDRp1ZRmGKnpDhCOSdhMOtQqAatejS+kwqBVYdeoaHEnCqDqtWpKMwzMzTazoTwDdzCGQSMiiiIvnWpHLcKqYhvZFn2y6OUV83MQRYEjjS4e3d+IMxCjMN1AcYaR6xbl8rWnThAasNx+w5JclhSkUzGEgZkxQPnLokuEKA6HXi0SIJGLJQ6ICc606FhXaicUk1icNzjcpP9kpdEdZV4/YbFF+Vb0GhFFVqjIHhzuuKfWgaIotLiCVDv8LBrgURmJ/3jfcv56oIFVRennLWNclmliRVE6la1ezrQnir9GpUSeUiwu8dKpduY7LBxv9rC8KI0rFuTw/jVFeEOxZMjfhzeU8OKpdlYPo+Y2GWh7krzVopBM7p9Kan80s70vF5IFmbM1RYZjgR0qUzVMUBQFbyhO/6WO+ACPoUYjoMgKspLIO8w062j3Jjqq81FS0/QsRIhCYkFvrLxypoNQTEJSwBWIkm7SJhdXJspEe6Zeo63JGaTbHybTfHGu0s/Sx0hdukoQuHTurPz5UEyG0bMDyAdqJ+FYs1wgFn37pUGfld63bVDdjF+8Wsnuaif3v2shy4v6VrDsVjOf3ppYPf7Na9Vs/Y8dAKwvS6eqw49RK9DQU9i02xfhK48f5/2rC7lzfcmwq7d/2dtAfXeAT22tSNZ+8YVjnG71UmQ3DpncPhbq29zJ185gjM//5XWePpEwzj6yoQibUUeXL8ymOXZOt3gJhOOcaPFyosWLWQ1bF+axrMCCPxwnEInjCkT5wt+P8s0bF3Hd4jy0KoFOb4RMs46KTCNv1jiJSQoy8NrpDs61+zBpwR2O4w7HselVmLUCpfk2lhWmcaDBxclWL8FInKNNLjaVZ5BpUqFVCXjDcWLxOOXZJuZmG9lZ5eTzfz/GL145y/VL84nGZewGNYcbPZxu8/HKyQ6qfng9P32xkv/eVZf0OL1wvI1jTV42zMlICjD0Yh0gRvDwR9Zz20P7hr2eZZmmhLS5RYdFnxrDnmnW8fjhhPRsrkU9SK7cZhBwhxKtumrAd7etLeHHz5/BH5X57GWlg867pjidRw82UZxuYP4QxttrZzpoc4e4ZWUhZn1q16bTiqwpsQ97D9V0+XEFoiwtHN2QyjDp2FXZyYF6V4pkaG/Y3skWF3870EhFtgmNSiDXqmdPjYPcniKwC/OsNDj9vHCijS5fmMvnZ9HpjdDpi7CsMG3coXvDcePyfKo7/RQNE+ZwoSgwq2jxD1OoZ4IMJ1k9U/j2LSum7dwz5dr0tiNzwPz74Y9fxuafJqtecK7Dx0sn25EHKI/lpaX2SzIipZkmnIEo+WkGjvVTOdx2op3fjNCWX796ll+8Vk2BTceur6bWMdpckUmmWYvNqCXDrKPVHaLJGWRxQVpKWQSAbz9zkqePtnDn2iJuXV3Em1XdWPVqbliax8Y5mSwYYqFnPHz9+onlG24oz+CNqi7m5VguWoPna1vzRt/oHUQgKg07gY9IClf9fCcFVg33bp3LRzaVDbPlO4/JMHoKgWOCIBwGkuvDiqJcMwnHnmUKuPK724b97vdv1PL9bWcGff6xPxxg931XsutcFy+caGPnuW5sRjVzMk0p4QqKJHH4W9ew9afbk5/JJMKV/rC7gU9fUUGLO8irpztZW5qeFAN4s6qL3+6oBqDTH+Gr1y2gxR3ibLuPLl8ETb2Tj28pR69R8VZ1F69XJlTh1GqRy+Zl0eoOU5ppJHsI+d5nTjtS3vcaPAB/3NuESgRFhmpRYEGuiZOtfZLI/jj880QbO6s6CcfkhHgDEIzJfPGxY/wy4xyNzhCSAr5IPGno9RKVSVGCA3D3VGr0R710eMN0+iJUtXuISAKSrPDEkdaUopiPHmzjH4fbUlY+611hXjvbiUkrcrLFm1whlYDyr78w6BrsrHYiCk6ONbsHrUaue2B7yuRoJIMH4FiTi4gETc4Q/kgsZZK+5gd9Gcu/393MN29KzQfqNXgAnjhQz61rS5Pv73xoD65Q4pfc9X+HqXzghtTfUNWFSoA2b5h6R4g5/Qyf481uHtqVWHdxBmN8dkANoFdOJ3LSVKLA3ZvLUiYznb4wzx1rRVESdW9G44mjbSN+//zJzkQ7PWFWFtv4xtMnaXAkCp1uqcjk8gXZfOXxkyjAc8fbKckw0uGNYjNqcPgjXL908ODe6g7R4g6xKM+aolrX5Yvw1JFmREHgfasLsfXLkTJq1TMivMFm0tPiH67u0fgpvW/4/mumkCaO4CqdQmbKtenfju4wKbIS/Q0eSAjbxGWFaL/46Nof3jDIUNeqRc72JOx3+cdXz+ZnrybGlkZXhJ+9eIaSLDPnOvx88tJyLHoN4ZhMJCYRjkk8ebiZmJQolv3+AUVH/9hTXPp3u+r46g2LeOYzm8fVjtG4/4UGPnjZknHvt2FOBhXZZnQaEUlWJhRBUd3h460aB5fOzaQsa2pDzifCj3a08cnBtWXfURysd3K6zcsNS3MTCrijbN/ijfHD58+wusTOkmkqTj3TmAyj59uTcIzzYqSOfiauAk43NSPEm++ucQz5uSsQY8fZTl461cG+eie+cJRwLI7dkLrS3zsBjg2RW6IA6x54DUGBdJOG+blmwnGFbn+US+dloADeUIxjTW7+981adBoVZ9t9FNmNCELChZ9j0fG9587gCkYIROIsyrPy8qkOVhTZONSg4pOXlo97VVuWe4qwklDgGQrfECXlJRIT22S+0DhLJASiEiFnKOktUAkKwx1iYKgHJBJwQ5H4iPLS/ZEVcAWj5x1S2JuMrgDNDv+EVxYbHKlddnVH38rtUMUsI3GZSFzuCddK/T4QjVHT5ScalykZQhCgN7xLYLBscv/Qr8kKA1OUxLE6vRFquxI5ULICb9U4EEUh5f98uNGNJxjDrNewumRwibNwTOKJQ83EZYUmZ5D3rupLkK/u9BPoySuq6w6wsvj81KOmglMdk2fwvF244aHj1P+4aPQNZ+GZIy2sLEr1kLS2t1GYn1pUd19N16Sc70hjN7/ekVgg+f2uWn73odW8eqYDo1bNPVtKe8aAkQ2HaVQDH5LepoqCMOG2/eiFSvyROHtqunnwQ2smdIzTrR4ON7i4enFuSm7ULOdPpzfMz185hyQrVHf6qe0KjFaPHUjcq+qZdsNOI+ed2aooyk7gLeAcUNXvb5YZSJt75AzbS+cNHQeaZlADAhlmLUaNCptRQ7ZFx6curyC9XxTCH+7ZyMkWDx/eWEKJ3UBxuoFl+X3hbLKSMBa84TjuYJw2d5i4JFPTGeSmZflY9RrsRg1n2nzUdQVQFHD4I8TjMm9WdXOk0T0olrX3rSCMP6E126JlUa6FonQ9X7iqgo9vmcMPb5w7qIiiCrDqVRg0fSfItWgRRSHZqZi0AgMDkzQi5Fl1fO7yMnQDep7+D59GJfDRTSXkWXWIgEoYeWDVi7CxPAOrIVELaCyIJGonLS+ypXz+mctyBm03Er3nE4BMa2q42E3L+kLWRgtGXF2amjsmj3LmW1cXUp5lZvPcTOwDDK24BJlmLZlmXVJCtj9XL8rhsvlZvG9N4aD6PplmHe9dWcjlC7LZPHf0BPCblo081FxSns7cXDOXzcug2G6ipEdtzqQTSTNosOo1Kb9Uq1KhUSdWaC8fRma4974eeH/PzTFjNWhIN2oG1cJ6u+P0R/nrvkaON7tTPp9dyBqemXJt+rej1Jb6nWHA4/lmdTe13anjUiwa5Wcvn+X+J08kC5XOH0E6t3SU9nzvxgVoRFiQY6amqy/9WAJOt3lpcASpbPfiD8d5/+pCLpufxXVDKFx9ems5WWYtX7pm3ihnvLDctDyfS+dl8b7Vhecdyjrcwt9oBKNxfvRCJc8db+M/Xjo7+g6zjJu+uY7AWNY6KzL1/OCWJSzIn/Xy9DIZ6m1fIOHt0QLdQB7QBMwGEc5A7v3TwWG/M2tF/rSngUW5Jk63J1ZndQJEFHCH4vzLnw+xpsTGwx9ew5EmDyadiisW5XDku30D3OFGFzvPdiErCl++bgEWnZrDjW7avWE6/X2hQ3NzzHzjXYu4/6kTBKMS1y3OYcvcbCo7fMQlhSsWZuMPx/CF45xp89Lli7AwT0RWFFrdQQJRiU3ldm5YVsBlc7No9YQozTQN6rDjksycTCO1PSFmGjHhNVGLYNGJ5NsMXDY3m7AkUWg30eYJoygin7i0gjZPiKNNLrp8UdQqgX+5tJxDjS52VHaj04gsL7Kxv85JXKWg06hYWWRjbamNX75WTVxOdFDFdiPdgSg7qpzcsiKfqs4AGUYt57r8hGMSoOALxclNM2AzadlQnsHumm4kBb7xroV4gzF++vJZFAVMGhF/VEIlCmSn6QnFJMozzdR0BbHoRPQaFYFoHJSEEeWPSvQUEkcgoVyWax1sinx006KU99cvzWHbiYTsdJ55cBdx44p8Xq/soizTOOh4q0vsPHs8IVG8vHRwR6tXCUkvTnlmatru1nkZPH4kse8QdgulGSayLDoKbEYsA6zSZQVpLMhNwxWMcuWCwUaJXqNi1QiqSsUZxjFJRmsEqMi2Ah1Dfn/5/Cx+d9dqQlGJmi4/c7LMXDo/m38ea8WkV7Mkz4peq2JdaToP7qzl3ctzkRWB2q4ABekGdEPIuOk1Km5dXUirO8TCATkDmWYd92y+OLvan79ylqpOP8+faOW/7lyVEro3Uyb3w3GZbfrOPROuTZMjyMbyDGRFYUWRjSce7/tufUk6O2pSdcoG5vO8UB3hD2/VAQnv9G/vWo3NoudfLyvn6cPNtPkS4W2fv7yUzw9R2+b1M+3c/cdDKMCtK/P42W2r+PAlCfn8/3uziu/+81xy28X5VtrcYQxakQyzjmzLYLneynYvr1d2MS/Xyr6vX4UoCnjDMZ4+0kJcUrh5RX5KPaqJsmiCjlqLXnPeEuVff9dC9lR3s2WYhc/RkMcYcTBR3unT9myrni9dO5/TbV7etTSPnw2xRqgRE0qhm+ZkcPu6kiEN93c6kxHe9m9AOfAg8ClgDvDRSTjuLJPM2TYPJ1q8yfcfXFfAP4+340nmmMh0+8O4e+KXyjMMXLskj9/t7NOoONrk5qN/OEAwGses09DqDvHRS/omXUrP4CUKAvNzLAiCwOFGN3esL+WpI820ukJkGEW+dM18Dje6ybLoCUbj1DmCZFq83HfdfLadaCcSk7h+SR4nWjzEJYVIXKLdE+a5Yy14wxICUNnh5+FVBejUKvLTh/YrKIC7X56GXqtGrRKYk2mkqjNIbZefmKRgM2g40ezBoBE51eohEEkYF0atihyrjhVF6SwvsvHEkVbUKoFITOKtGgd6VV/lYlGAmAR6jZpILCHN7A3Hk+7omCRx1cJcKrJNvHami9PtifMUpBvZMjeTaFzhWLMHlShi1akpTDfSpAQx6dS4A1EyMwysz7bgDcU52uhkpzcCKBg0IjFJYW6OmcJ0IzkWHWa9hrgs88juOrp7jE2rQU0gGidzwOB8rMXJlWl9E/7+VZyz0wYbAqtL7BTYjGjV4qBwvBdO9tVkOTtEWFOsX9iaJxCmKLNv9fZ0u69vuyHEBas7fciKQqcvjC8cT6nxk2bU8ovbVwzeaZKJKbC/zoFBC6EBaQUaEZrdIUIRiXSzljWmRKhauknLF65JrS9xWHBxw7J85uaYWVmUTnWXf5AR2J+8NAN5aVNba2em0TsRVpSpn1BNNofd092C6UXu1zP0N2her+ykNCeNtFYfkWicbIueYExKqfME4I30PVz9ZfiPNXvwRyX06kSl+K7A0DfGD7adSbbguePt/Oy2vu9eOpkaJnfZvGzsJh3pRs2QOaEAx5s8hGMStV0BXMEoGWYdNZ1+HD25RWc7fGyaBKPn9PhSlSaVOVlm5pyHt9isV/PV6xZwuNHF1QvHEng1Pjyjb3LRs7I4PSmJPlRYu4KCgICiKMm52CypTIbRE1MUxSEIwilgBfAa8ATwL5Nw7FkmkWt/+WbK+7/sb0l5r1cLSYMHoM0b4SMby1KMHrUo0uAIogCdvijfee4033nuNJBYYVxZlM77f7cHGfjla31RjiaNyBeumc+Dr5+jMyBx9x8OMj/HTJpRi7+n5svZdh8ldiOiIFDZ7uP/3qpDLYqsKLaBAs/3m1ArgFGt4tXTHbxrWWrsd3/21XTjCPQZPd5wHL1GoLrDhzuc6DXOtfvItuqwm7Tsr/Om5M8krkeEbm8YvVogHJUI9hTQVKtAqxaIBhUcgSg7znWz41w3Vp2Q7JC6/dHk4Ov0R3mtsp2HtvsZqO9e1elPeS8KcPcf9uHtaaNagNNtfirb/ClubRWJkKe4ArtrnBg1bqw6Fe3+wQn54Wickgwj0QEJQg5v6kj7xtnW5Otj/YzkXr725Ink609fnloluzCtzxCJDWG59M+MqnWEWNJPrKi/jPpQWl9nO3ycavFgM2qRBsyC91R3csfDBwBYVZTGk59OTTCOxCXOtfvJserOu+hahlkzyOCBxCBU1eHn/z12mD98fEPy853nuqhs87Km1J5cjT3c4CIckzjW5GFzRRZrS/tyeWRZ5pXTnRh1KrYMkB2VZYXnT7bR5g5z+YIsKgaE/Jxs8bC7ppvSDBNXL8qZcKjKTOCv+5uSrz/2g1d4ZgZ4MMbKdNZvmAnqbQ88uYM9PcPGvjonmUA0LvP00RYc/ghrS+1snZdFmkHLvz12ZND+B+ocbCjPwKxXc+e6EhocATJMOvbWOJL9n0YlUtnRt1DS/3f/29byZN5OeaZxyG160arFUb0ki/KttHvD5NsMSY9jlzfMf++sQQayLNpprY1Udt+25DgznZ6+JQVpswnz00hiaFfYWeVgZ1VffvY9l5SQZzNyxYJsTrZ66fCEuWJh9nkZuW9XJsPo+Y0gCJuAR4HtgBMYPoZqlmlhLKo+4QHlfZcXprHxx68N2GbkJddvPXNiyFjTQEzm7webiEiJZH0BaHYFuWxeJi1uLXFJoTDdwJEmFwfqnRxrchGKJo70yukO3rUkdfJnUEOuzYA/mpget7iDHKhzsXFORkoC5e/fHKykHo4phGN9vzUmK7R5wnT7o0MKBgC4whJ/O9BEmlGTLD4aikmEooOn595I37H7X9F2X5QOX3RYsYL+yApJgwdIVl4e2DxpwEmCMZngMMoGwZjCkQYX//tmXcrnX3n6DB/YUJ583+wZ+7L6e3+1gyc/uzX5/qmjfYapf7gqhD2cbXMDfcUHW9wjK151eiOEYzK+cAx/JI69X3/9rWdOJ18faRq8Jvjq6U7OdfjQqBLqbUbtxLu+V8+MnFC9q9pBLC5xpt3HuTYvzx5vxRuKc7LFQ4ndSLs3TEW2maNNbuZmW1IKxnpCMf73zTrequ5OJlL3N3wcgShVPQpWhxvdg4yeI40uAhGJU61eNs7JwKIfX70YSVY42+7DZtRMWCJ+Kjg23Q0YJ1+85PxCjSZK+QxRb3u5ZujPY3GZujYHLQF49UznsPsfaQ5QkQ0/eM8y3qzuBuD6hZkp/V8gGifTNPT9/esdtfz2zlV0ePzcsT4RiXC6dejntq4zwG3/s5vyDBOP/cumIbdZUpDG4nxryiLCn/Y2JIpJK/CNp0/xvWdPce6H02Nw9B9TXjzexHXLZkU0Lnb046hs8Pu3GvjXreXsru5OKh8ebXS/I42eySjR/YqiKLsVRTkLLCMR2vbeSTjuLJPEv/7p0IT2e+yTm8Y0Qe/PrnPdw36XkAwW0akFRFGg0G6kqivA0WYP9d1+DBqRnee6Od3mJRCVkUlM8mOSwnPHUwfIAruJ1SXpvKsnZvV7z57m0f2NfO+5UynbOfxjk46VlYQ62EhEZYjFleTAKyuDjZDRmE6HswI4g1EiUqqhdj6lOv/fxlQxAs04ehRnIPV/M5oeQ6c3TCQu4wvHiSupV/6K+X2rrCbt4EbEepKbJDkxsT8fgtGR/+t6jUCjM8iPX6jkwV21HG1yU9Xp52y7l/94+SyvnO6gwxvmygXZbK5IXR3+x8EmjjW76e65bwca1elGDXlpekRBYGHu4JogC/OsCAKUZBgxTcCw213TzUun2vnHweZkG8bK9soOHn6jltOtg72D7zR+/JZr9I2mgHs3T8Y65tSgVYtsXZBFyxjE/GQSstS9zy1AaEC/ZdSIg0oE9FL/43fxqb8e5rvbztHe2gzAovyhc1Wu+cVOOn1R9ta7+NLfDg/bpoFe0/Xl9pTPojKs+M7gcgEXmlmD553BEIKyIyIisLIkndye8WN+7vDCIBczk2H0PN/7QlEUh6IohxRFmdxKdLNMmKt/8hIvnGofcZsco4h6wJ3Q+3YkV3lGv4Xg3q7/3i19+T1L88w89N4FqHu+7x2+5mSaubxHHScu9cSeCgIatYhBo0KWBk8qJQV+fdtyVhamsb4snS9fM5+vXLcAc89KdqRnn4GGy/FW/6Bj9W/vuBGUYfedSmlIgUSIoIrE/2bg1GYsp9WqoDzTzN39crAAts4dZocxEDWkSiwvLOjrSEfrXNpdqROWQvvIQgKeUAyFhGduYEjeytLM5PnWlAw2Bq5alMP6Mjs3rcgft/djIKOZTGadhj013UiygqwoCIKAKCQqv/fen/vrnLx6ppO/HWxElntzVxSqu/zEJZncND3vX13IVQuzU46tVoncvq6Yz1xRMWQh1TWldv7tirm8d9XEVJx6Qx9lRUmZcI5GIBLnWJMHXzjOwQbnmPc7ceIEpfdtG3d9md59ZkpdmpnCZ666crqbMCyyrOAOjl4HCxL92db52awrs3NJRSZXL8phzoD+QRRFLu2nttg7VtX/+F0p98XWB0+lbHP/NX1ebQCpX+5Dp2/sSTV3ri/l81fOSfksNMqCyFTxk1uX8p5VWdx/w8JkfzJe9tU6+P2bdRxqmB6DfZbJRycm0ha+dNVciuxGjja5uX5JLp+5ouIdG4Y4GctCTwmC8CDwJJCcYSqKsnsSjj3LeVLlGhxjpBKgfxmUjuDgjrr2x+/iy4/t5R9Hh67bA+DoN2dVgONNTu7aVMYLJ9uo6vBS2enn3icrk9tIcmIi1eQO4QrFKErX86FNJRxv9FCSYeTaxXnoVAI7KhNGmkWnIhCRkAGTRuDVyi5ElUCR3cjSAQ/sF66ex5vV3WydPzblmYmu9Yd78nmGO2Z8jAfWCImk+PEQisl9XqYhzj0aUQn21Dr5x4HGlM91hom7uAfWsmju6ouxH234NwzQru32BofZMoHS491RFNBrU337v3jlTPJ8O6rcg/bt9gf5yYuVXLkgi89eNX/Q95NJpz8ht2s16qjINLGkwEZckrGbtVw6NwtZhrouP/883kqhzYBZp2bXuS42zcmgwRGkvtvPorw03rdm+BXbkWqInE8dpksqMjFoVdgM2iGFE060eJITyv4LIgaNiiK7kSZnkHk5qSuIv7htGZ//2/Ehz3fjX/ruxdL7to0pH+H+xybmub6QVH5neoyPxtpzo280TciKMiifcDgO3r+VDEvCB72uLLGw4valZkrt+vJlrPj+azz0Rl2KwTMaJbnpmHu8waWZZu7dUsqXHz+BzajhkX65eKNhN2n51OXzaHMH+MuBhBDD2WkKbwvHZIrTbUQlecJj295aJ7KisLfWcd5KcBNFURRePdNJiyvIZfOzR9/hHYx5iIiGgdy2tpA1pRnsr3fy7PHWhOCTovDBDaVT38AZymQYPStIzLnuG/D5FZNw7FkmSCAQYPH3dwz53coCIwebh59gfvHKhCdgJINnKG76zZ5Rt/FHZfRq8BNHLYo88M8zCMDPb1vOW9VdPPxmPaG4klgZV4l89boKznX6ifckrhs1agpsRgbWrlxWaLsg1efH61IejrEaR70oTF5o3K+2pwbcP3ncz8/vnNixugZ4a7rGkcH9/OnUFcVRUnrop7FBeEDeUmXHyAbTVf/xBnElof60JN/KFYvyxt7QCeAKSfjCQdSiwL9uncPOs10YNSqeOdpKuzdMozOQFPBoeqMOUUxI83Z6I0TjMgcbnHzp70e5fV0RSwttQ0pZ9+f5E23sOtfFLSsK2DAnA0VRqO0OYNGpxyXcoNeoxpyQ3d9IEUWB960uJBqXU3KUAG5ZWcTWiizc0SilGYO9cGOh/2T2gdtX85ejM9vD89vtNXzhhsFSylPNDY/Ujb7RNKFWidy8ooD/eHl0w+xMp4PNltTAW9sAZbUV3+/LN+1/Ly7/zuB742t/38+jhxM5Pae/dQ3+Ho+Mr93LTSuLuGllUcqxehnNiJr39eeRRtjudIuH+58+iU4t8ovbV5A7RQqM3362z5s1UFxmrDQ7/Ww70c57VxZMVrPGjSMQ5WRLIifzQP3YPcbvRPxRmdEC1B7Z18wj+5qT7891Bqhq92HWq3EH42SatXT5ozQ4ApRnmrluSS5ZltFVCBVFYXtlJy3uEFvmZnGm1cuLp9pYkGvl7s1l6DUqjjS6ON7sYWlh2ojlIi40k2H03AV8H1gDhEiot/1oEo47y3kwnMEDjGjwANy5KmdKw0biksyaknSePdxIR1BGBdz/1Ekseg3uYBSVKCArkJem56YVBeTZDLS6Q5xs8RCOyZRmGikaJRQKIBAZWyjFdDCduT1LCqycmaRjPXm4hvdfMmf0DSeZ/91VzdqyvnwivWpkg7S/kbn9dPsgo8fhj+AJxSgbQTZ6vMQV8IVi/HVvI23eMCIyMSkRiukLx1EU6PKFWVpgo8MXprjIhKJApy+CQEL1yheOc+2SXN67qnDY80Rjcf7jpbNE4hJnWr0882+bOVDv4q3qbkRB4M71xWMayM6XkSaLNoseGxNXzes/qR2pb3rzdBd3PbIfgCyThgPfvGbC5zwfqruGDqudav61EH7XPPp208VY+m2Au/7nBKvyTvChLct5z6pCPvjQHt6qHX0S/JlH9uMZYuGl1+ABWPS9l5OvGwY4nsYz7vXftvS+bfzmzlVcuzgHdU8Zg8o2Lz99+Swd3hCCIPDY/kY+f/XUepkBNn59G3sm4HH6x+GEauef9zfxg/cum+xmjYk0g4Ysi44uX+QdmWQ/HsaTP9ufFk+ELz52DKse3GHQqcCs17C+zA4CVGQa2XGuC1mGFUVpuEIxVhanYzdpOdPqpdUT4rK5WRxvThinu851cqzJQ4c3gi/sZMvcLJYWpvFWdTcxSeGtqu6Lzuj5G/AS8AMSRdg/Cfwf8L5JOPYsE+B8DZbV/7530Ge9imUT5ZNbSnjojQZEETZXZHK61UtnT1idBKTpVciA3azjc1fNZXGehTybKblqnG8zjFtNyh2MMfVTvbcXNi185doF/OObfZ+dj88j3zrxtMC888go1KlT78YlhWkcbBhbJYeynNTQSHcwyl/3NRKXlWQozWQgANlWHadaPUR7XJMalYheLSRysHryfDbMsWPQqvnYxhJ+t7MGdyhGhzdMNC6TbdERiIwigyeIyZA2VY8aRO8+sqIMqTA4GfQ3bCba5/TPv5gMqd27/7I/+borcGEXPfpHFf72I+sv6Ll72Te8jszbjsNt0PHyWeq6A2MyeAAONaVegBwd7Pvuu8Z8f35oJfxpsIL2mPj0Xw/z0AdWcs2qfCrbvHz3udN0esMEozI2k2aQaMlUsfuBGy7IeaYCjUrkznXFRCUZvWYc8mTvQEoyzIxBE2RI4oCzZ3EgJEEoEOPVyi58EYl/b3LjCyfGD5UoYNapsBo0XDInkxdOtqFRiWyv7OTyBTl0+yIsyLUSiEi0eUJkW/Xk2xKLW+VZZs62+yifYcbrZBg9eYqifL/f+88KglA9CcedZRoQGZyLIQLzckx0+CK4gomHodimYsdXrmXLj1+kxdu3hwr4+g3z+P7zqSEMeq2Wf906hwq7gd/srKHTn5ow+tGNJdQ6I2RbdVy1MHdQiMzv36jleIuH96wsYOsYY30HhsANhwDo1RCJD52Hkq4F1ziLxqXpRDyRkePXNQIUpGvo9kvJcIvJRAMMnPZtmZ+TnBj3Mu88xH5saROr3g1w48bxnbj/vXnv5QtTvuv0jhxXt6Y4jcNNHoxaFdcMqFIdiknJAoi9nf35YlAnBA1WFNmodwSJyxIIAjZDouhsly+MPxon06Tjns3l2IxaVKLA5QtyaHKFWF9mpzTDRGmmaUjBgv5o1SLfuWkRb1Z1J2tWbZyTgSgKWPRqijPGtro+FpYWpHFwnMaJOxDmrt8fwB+J850bF7N1QerzO5yx89Nbl/GVJxK5QKe+e23y81Pf3DqsJ/vdS7J58tjwUsiTTf/JdJZFR6FNz8K8iYXwTQb/+Ob1zLl/+hXERmJpvoUTrb7RN+zhV9tTpxMFVoHv3LKaTzwyuDLGK1+8hiXfeSn5ft93B99bA4UOJEni2WNtlGWa+P5t7wK28acjEzPC7/vnCa5ZlY87GENWFDItOq5YmMVnrphLmkE7+gEmyLdvXMR3nzvNPBKqoqMpYQ7FwlwTle0BVozS30w1oiigF2cNntHQacQJGz1DoRIFFBJ5171TJ0VJiPFEYjLeUAxJVlCrEuPkB9cVE4nLGLQq1pbauWN9EWadBk2Pp/P6JblsnZ+FYYYZr5Nh9DwhCMJ1iqK8CCAIwjoS9XpmmQaGW9EaqgMfatu/fGItf993mqeO9z1OMnC2M0D/Ar+dfon/fOUs7f6EmplaBJUocsOyXK5ZWkCbN8rDb9Yntz/Y4GROppkvPXlykGGhFuDaZcOH73T7w7x8ugNISPquLLJxosXD8iLbiEpcZp1qTJLSCqn5IgO9WuM1eHQqYVSDBxJCBvXOwavRGjEhrTzcEVRCryyzgDcy/Cr+UOvc2052cMXC1En/5efh3Hj38uKU90MZWsNR2dZGQuV+bOg0IqGeXB7NgGT97928jI/+IVGcdF7W4BC196wqQhBFCmwGsgZUTs9LM3D5gmycgQjryzIG7TsRFEQQBOwmHevL7Zxs9pBt0XHp/CyWF9j47121BBwBEMCqUyc9NWtK7ahVIq5AlHVldiJxmWB0dEPskoosLqnoM0D1GhWXzZu4QTpenv/sOm741f4hv3v4zXpO9shY/3ZH9SCjZziuWpTDw6Y1FNmNmHR9Q1WDd/gH8ud3rOWaJS3squzgh+9fNY5fMH4G9p9d3giiO0yzO8y5di/zhpAUn2pWz2CDp747QJZFN8jgef6zm1mUnzbkeNTiTl3M0Irw27s2sv3s0DV3fvHSqSE/Hzj+9X//zadPsL2yE5Uo8Js7V/H9297F928b008axM3LE37zDXMyuLm7gC5/mA+tL51SgwfghqV5FKYbqcg2T1jI5IXPb53cRs0ypXR6w5MygQfItWq5bU0RSwtslGeZ2V3dRVyGudlmBARWl9pZWphGmlGDKxjjE1vKEEUBQ4+gkCgKpBtTx1VBEM6rHt5UMaEWCYJQRd+8UAC+IAhCGwmvWRHQMDnNm2WyCUTiOPxRfrN9cDJp70CwcU42Tx1PHYCUAV6TcBz21DiSNU8UGZaX2FiYZ+Wrjx9PVPxdkM0Pb1nCtb/Yye5qBwfrXYMm8iatigP3XTpim236hJJUmyfE/Fwr33jmJJ3eCIXpBn72gRXD7heNyxO6wc83lC8yVhfTMMTk4eWerToVBTYj/mgcs0bB2zm+0KXenJH+PN6QKK7Vi1oYu9DC2XYXK8v64nXHE1B0qHZ8XpX+cuSnWj3M7TeprOv2Y9aqUACLcfAEQ9cvQT8SHxw6saLINq62jIZKTCiaxSQZo1bNgjwrC/OsfPnahIfqiSMtROIyRq2KmKLQ33TvbUu3P8Jf9zUiyQqXzc8aU1x0myeETq3CbpraSdZAFuVn8ecPr+N/3qzij/emFnh8+PU+4Yz99WOXw7WbtFy1KGfwufLSKcsw0uAMsnnOYCP1uqUFXLf0widj9+/bmpy+aTF63Bf8jGPDE4rx1JEWzLrBPfINv3qTDaVji/kXgZt/mxCGNaogKCUWgf50zzoA3qgem/iONxzDE4xRmG6gw5voDyVZocUdZukQ62/RqMRLp9tZXJA2bLjOHz68gq2L+u67O9cXD7ndVJBj1XP1oonnzM3y9sPhjzK4dwStAN+9ZQlLC20TkqW+anHusN9tKJ+cRcHpZKJm2FWT2opZppyxxDSPJy5fA9T0kyeOA4tyrfx5TwMt7hA2o5Zuf5gNP+5z+kXiMkVpGlo8MTItWvbff3XyuyXffB5/j4bz8gILz/zbpcQkmdfOdHCkwYUgKNy7pYzLF2Tzkf9LrOg7A6O5YCZmfExPpYVUVEIiTGEg3oiEt2PsoSEDsehU3L62iM/2++yuTal1KzaVWthVlziHeZQe4saVqSFqKhI5WmPhl3eNbyXeqBPxhxP/nWVFqZOkm1YUJLwnEYkblw3utMuzjPz9QCPryuykGQZ7B73hGL5wnIIx5I2NxSgORWWcQoROb4jDDS7cwRjOQIQXT9q4cmEOWRYdRxtdLMyzUt8d4GSLl0X5Vqo7/bxV3c1VC3OYm2NOLip4BtQ36fSF2XWumyyLjkvnZiIIAidbPLxyugNRELh9XRE5Vj2RuMRrZzqJSTJXLcxJ8ZhMNpsXZbF50WDv0kiBhwP7nP4r8J/45TZeaRv8OcDrX758wu2cLAaGSfXnnkeOUv/jC294jdSm6WxH730cGMZruXcIY/jGJdlo1AJPHk14+f94axkfeaJPnS4ogV4tcuRb1yRXnF/64hWDBAZ62wNwqtnDf++qJtuix6TXsK7Mzr9cVs53nj1NfrqeaxcPNY2ELz1xjEMNLvQaFX/5+Hpy0wz86palfPbpEwB8fGNhisHzTuVQg4uaTj9ry+wTFoXZXd1Nszt0wXKg3q4MN0+5e0sJd6wvuaBteTsxoRFQUZRZT84M5LXTrRfkPGpRIN2oIjAg/+FPe+rp/SQQibOuNIPjzanV2Zs8MTRCQlnp/qeO8sB7VhCJRJIGD8CxFh+Hajsx6PWcaHLxyN7E7Xa82cOVi3K599Jydp3r4soFQw9QvXT5Y+eVpH++6NQiUlxmIlki463hM1b8UQlfONVYvO/5Wm6/tC9HptfgAfCP0vgfbTvKD25dm3w/Hr/TFx89zLEHxh43rxUSF0VgcO7NoQYXalHAalBzrHmwoMEXHjtCsyvMsSY31y3JYUlBn9HkCcX4894GonGZTUN4DgYyln+NDASjMntqXbiCicry9d1BzrT5KLabeL2yE1cwxuFGNy+fakdBoNUdZG+tEwV49EAj//fRtWwoz8AfibO+PDUGcU+NgyZnkCZnkPk5FnLT9MnCj7KSKAKZY9Vztt3H2fbE//OYxT2sHHWnN4xOrSLNeH6FWyeTXoMHxl7DZzQmUzRh4HF0f/x88nXeNIWxzwSDB2Dh/antsBo0LMi1UJJh4hevVo26/2evMPKFaxL9ys9vT3zm9EeBVEnucFxm4bdeZM/XrsCsU2PRa0Y0/G757VvEegywknRDMhzseIuH4y0eml2hIRXm2nu8QeGYRJcvQm6agZs2FHPThoQ3p/S+bfztYDMnvj89dXq6fUEe3d/MTcsLKJlEBcrxEI5J7DqXCDsMneuakNHjDETZV5cQrXir+iJS5biAPLirgb/vbSCiwMe3lLOsMJ0OX4j5uWksL7ThCsYSc7gLHA0wU5h5AXezTJh7Hpmg7MwYWF9s5lijnzAQlxWWF9kxaFQ8e7xvZtJ/Groo38IzR1uGPFZMgVPtAU61B+jyRDjaMthzUdXp5/JFVhRBQAFEQSASS0ypt8zNYsvc0fMVJpLMOZlExliI70IiK5BtTu3srh/emz0qDtfEk8a14xQVc4Z6QikBh9cP2JLf5aXpiUgSoahEftrgMI8uf5S4ApKk0OYKs6Tfoqw/Ek8WTRzdezh2eq+1PxwlGgcUmWAkjjsUTeYqRKU4ORYt7b4Y6SYd2VY9re4QWWYdgiCwcYAR1ukNY9CqKLAZON7kwW7SJj1Xq0vSCcUkDBoVc7PNdHrDiIJAKBbHpFUPWWwU4ESzh1fPdKAWBe5YX0ymeWKah+GYhDMQJdeqR+yXV3BrBTwxA6RtBnoAhlOfmwyDaPu3rzvvY0yEq8rg1RlQqic04NlWiwLXL80jEh/bQ1+cM3hBy27WMj/XSFV7cNAq98Yfbecr183nQxtKcPiHLvgViUSI94vTbnCF+H+PHUkJ5b38p69TPcT//yvXzOPBXbUsyrOytF89uI/9fi+vVyVC6nwxWPj1bZyZhgKlN/7XHhz+CA+9Ucfh+69Aoxn/4oXLH+Vgg5ON5XbME8hB0qpEGp0Bqjr8XLdkYsuNph6lMG8oNiav+yxD4+wZxn65vTapKmkzavjA6iJ0GhWiIPD+NYXjVsS9GJg1ei4SpnqFb19jX90JlSiwuczGt58fvsjc4QZPyop4hg5ckcEu2aMtXrwDVu31anj8aBs/eP4cSwutrC2w8Ea9h3ctThg6TY4gcVmmbBQpxPNMrblokQdkDL3QPvFjhaXRyqMNz9CpyGNj5zkHVyzuC74/3uzG4Y+hKPBaZSdfvi5V3U2vEQlEE6o08waoaxXYDGyck4EzEGXTJIdUNLlDeHtELXxRhScON/PssVSP7Oa5GTgCEtvPdpJt0RKXJLItWg7WO1lT2ufhOdTgYte5LrRqkfk5FtQqAblHXedUq4dzHT5WFKVTlmniWJObl0+3c6LZg1olUJZpIneYIqXdgcQkMS4ruIPREY2eEy2eIb0lcUnmxy+coa47wDULc/ngxr7wipEMnrFKVk+WZ2YsTIZX6T2/ep0Xv3Tho8BngsEzHIqi8PcDTWPattA6OB8qGpdJ0+swa0N4o4M790hMpsER4NbfvDXou+HGx4G5i8M5tteUZfDwAJGTTT96lVZPqoEVmqZ1rjZP3yKKJxwjcwJGz8ofvAIkPOl1E7j/veEYLe4wWrXI6baxlQ8YiE6t4q4NxQQi0gXPS7xY6Q2TdwZiPLirNvn5f77aN38TALNOJMOso9MbxahTsazQyrffvSTFc7i7upsd57oIRuMsLbDx7mV5mHRqarr8HG92syDXOq3qlWNh1uiZZdxIsjKiwQODQ4AcQy++IcXj9EaCiECR3cCGMjt/O5TwEu2p7Yv1/seRdt6/zsFPXzyLSoR/3VoxZvnqWfr4+StnJ+1Y1yyfngDC9QMSKmt6ikEKAnhDg6cujkDfZ08fbuZzA4oETlWCpnNArRhXMMZABc99dW7KshIqOQfqndR2BdhT48AVjLGiyJYsdtjVI0ARjcs0OYPoNaqEoRKK8srpDhQlkdz68S3ldPkixCUFRyCKWhQIxWT+6/UqLp2bRZs7xMFGF2pR5GOXlLK21E44KmHSqSnPHHtNhf7GgTMQ5fWzXcQlmWCkJcXoGY3hDIwLaehMNpXdw3R471DquwOUf+15dOqxud9v/+9jfPJSL1+7YRE1XX7MOjU5Vj2t7vCQBg8kZNqfPdzIGIQzh+X+TWNfxBlo8EBiDLvxV28Qjst8ZGMJd20snXhjJsjRfbu56qqJG9wTXSs0a9VkmbV0+6OU2CceYnei2UOLO8TGiyBpfrIJxyS8odikF5tWAF9ExhcJARCMSRysc/N/e+r4zo1LgEQe6SunOzhQ7yTU49XPt+nZMjeLV093EIxKNDlDLMi1IAjTHGYzArNGzyzjZkEGVI5NJGdY9GqRcFzG0bM0JgCfuWION60o5IWTbcPu9+j+Rto8IYxaNbVdAbb2m7seanDR4g6xodxOtmVWyWY4Kjsmr1q8Nzh54WDjoWPAhONr181nf60TRzDKA+9dMuK+Ft15VEUdJwMnEGpRQCMKRKW+mdmRRjdPHmmh2Rmk0xsmIiX221vj4GN/OEC+Tc+ntlZwvNnFiRYvt64qYMvcLN6qcWAzaDjR7MHhj2DSqensDPPjF86weW4mywptaFUiu2u76fJGcAWi/GF3PSatij21DnKsen756jkum5/NquL0IXMZxopBq8KgFnFGJdJNMycvqD9jNaLezsbWTBEyGIgvEscMhMcqCwn831v1LC2w8ae9DWhUAl+/YeGIRtOG8gz+88Uz426bVi0Q7WlXwHh+i2gycLLNi1Gj4jc7aqbF6Hn4XJTzsHkmjFot8pNbl9PoDLAgd2IRAK5AlN/uqMbhj9LgCE5yC9/eROMyf97bgC8cZ1XJ2NQOx4vYT0BJqxGp6BdNY9FpSDdpMGhUyIqCWadOzrNy0/TUdiUk6WeywQPTZPQIgqACHgIWAtsVRfnGdLTjYuFCD3IbyrKpcXUSO48VtfCAfBcF+OfxNj5/1XyK0w1JhayBSlm5VkNPHKqCVa/iTKuX3DQdILDrXBeuYBRXIMJHNpVNvHEXOf7wyIU8x3esySnmOV5eO9PKxy6dk3yv0Wi4c10xJ1vcXD5/cD5AukGFqyfRYGnheRQmOk/0GoHSDDMnWvsEPl6rbMcZiCcLpPbe913+KP5onPruAM5AlFZ3GHcwytNHWrliYQ43Lc9nX62DQw0+0gxadGqBs20+arsD+CNxvnb9QnLTdHjDMRocQSJxmXybAVlR0KpVCIAzGOVkiwenP8LHNpcP2ebh6G8cqEUxaUy/fHrqi4PGJRl3KIbdqE3JH5oMJks0YTqYiQbPRLEY1NR0J+6pmKRQ3x3AN0xdsnnZ5jH/9lyLmnZfX78V7WeICfL5xadpAEUQEATIskxPeNaf7r129I2G4KalWWw/2821yyZe38usV7Mof+LFTTu8Yao7/EiKwqEG54SPczESikpJAZ8Oz/mP4XrApAObUYtGLbKkMJ3F+RZ2nOum0GZAJQoszLVwrt2L3aQl06Ln8vk5LC9Kw6jRYDOq8IVjhKMSl83NIhKPs67ERqc3EeJo1qlxh2KoRAGtSpwU5dBIXMIfjpMxwbxTmD5Pz/VAs6Io9wiC8LIgCAWKogyd9T7LjOMPBzux6QXc4clNmqntDvIvfzqAzaSjOF2PDMzNMrO7zkksLpNh1nKg3sncHDN7ahx8/amTiKLA1QtzuHtzGcea3Jxu85Bj1XPziln50OHIM03eY//rXc188Yblk3a8sRIdMDn5ybZT/O6NegCePd7Gye9dn/K9Rp0Q0xaATPPgyYgnGMUbilOUMXFvx1jwRWS8oVTvWCwaSxSjVRJFfo0aFTFZRq0SCEYkYnGZQCROMJIY8Ew6Fa5AFKteg9WgIRKX0KkS9ZtOtHgQ4mAzaGjzBNl+ppOz7T5y0/TctDyfkgwj7mCMO9YWEY5JPLirlgP1TpaNsZ7D0oI0Dg5hECz81ovnf3GGwBmIYtGrk1W+IZEb8tj+RhpdIVYUpXHD0vwpOffbkZPfuZYl33lpupsxKTj8MT6wughnIEaaXs3lC3L42pMnhty2tmvs3uv+Bs9AXqvs5nPXpH7mCycmbmMptLiiJI1v3biYynYfNy55e92Xtc4IGrWa2s7p8d5DQpCmLMuMNxRjdbGdx6atJTOPNKOGky1uzrb7+PTlFed9vDAQjoAjkvh/V3a18fiR1CibR/Y1k2vRUp5lZlmhjRdPteOPxKnIMtHmiRCXZRbnp1HXHaDVHUKjElmQa2FhvhWLXo0rEKXbH2VxvpXb1hafV1heNC7zl72NeEIx1pba2Tx3Yvm302X0XAL0LsvsAFYAs0bP24jJNnh6eflMFx/aUMyCvDR2nu2gyZVY0Six6YkqCp2+CCpRwBGIIisK8bhCgyPAuXYvp9s8uIIxonGZN6rOJ03+4uZc19s/3yDkS53k/Glvn4q+Pzp4tbbTl+jYFeD5E218+sq+8ItOb5ivPnGcYFTi1tVDVCacZBpcqat0S/PM1HriuIMxbAYNcVnBGYwSl2Ry0/RkGDUEIhILci0YdWquW5KLVa8hGI3zZlU3kqzQFQrz0uk21ILA+9YWca7Dz31PnCQuyWRadahFkb21Dn7y4lkEAa5YkM0nNpfhCcbo8IQ5FFc40+ZlYZ4VpUfy2mrQTLi6+2Sw81wXH/nf/QBUP3B9MrcpJsnsONeFLxzHGYhMutEzGV6e6REN5m1r8Hzzhnl8f4g80Tybge/etJh5X9vGr7YPr4gxUtTcp7eW85sdtcNv0I9H71md8r6my88/j7WhVgl8YE3RqJO2339oLVazjqWFUxN+NBZOtblZUTT+fBhPMIZWJeI6DwXLynYvtV0BVhWnkzuEiuZopBm1/OwDy2hyhlhemMaXJ9ySi4/99Q7298h5//eusd3Pk0EoJuOLxHn5dAcOf5S4LNPpi+ANxzBqVTQ5gzgCEWRFIRyTCMbi+MMxHP4IoiDgC8eJSQpdvsh5GT2BSBxPKJEj2+oOTfg402X0ZAC9GepeYNAyoyAI9wL3AhQXX7jKxrNMP6+c7iDTrCXcL5IhpiiUZpgJROKYdCpuWZHP8z1y2Va9hpM94UIqAYxaNVr1NBXKeBvQ7Y+NvtEM58SAyAf/KIWNNCLJcMzCAbkr9Y4AwWjiZuutaXMhMev1ZEpxtKrEPesLJEQIDGqBsiwTkqzgdIawm3XMyzGzv87JM0db2VKRiT8SR69W8erpDrr9EXQaFZWtXvxRCZUoEJMg2LPNrnPd+MIxtGqRdk8YTziORa/BoFVj1KlodYdYmGflhZPtnG33UZhu4P1rikZp/dTRa/AAVNz/QtIYEQQhqTA3UXntgfznbcup707kEDgD0fNWjgpMRqMuInpNZ0EAZcCjatapKB1FQGMY7YIxMVaDB2Dx93ekGL27zrXz4I5qVCqBNUVpZFlGDv1a9oNXpz008pbf7J1QGzbOyeBki4fVE8wXCcckXjrZgawkBFQ+tGFiBTIzzXoyzbM5uQMpshnRqlVE49Kk9XsDEUlV2E3TCywvtlGeacIfjnGk0YMowrWLcmjzhmlxh7llRQFVHX5erWynON3Iwjwrc7LNFKYbqenyE4zGKc0wMzdn7CI5Q5Fu0rK+3E6LK8Ql56GyOl1Gjw/oXWq1AoOqlSmK8hCJvB/WrFkz4S5vpDjf6e6cZknwoWU2/nTcDSQGR5cvkiwG18tVC3L41o2LOdTooizTRLZVz0c2lfHcsVY8oShVHT6CUQm1SuTmFQWsLZ2+vI2ZjkY1ebqq0yVOefOykQvTDuT+GxbxHy+fYW62iZtXpnpzVhWls7YsnRZXiPevLuQHk9nQMWA16cnL1HCm1UurO5gMY0s3aPjwhhJ21zjJMOkoyzRRnmnmn8daicsKh5tcXLckj05vmPIsM75wDAGBDIuOlekGXj/bxcJ8O9GYwuFGFxkWDbKisCDPylULs0k3abl9bRGvnmmnIN3IymIbu2u62XG2k2yLjhZ3CFlWxpQzc/LbV7Pku69M9aUCQKMSeaxH+vjVM53cd/3CUfYYHZtBCyQU8QwD5fVmOW96c5sHGjwA/ojE1544NuR+wWh8TGFlAF+7fh57q7t4vco16LsbFmfz/Kmx5ZptP9rOL3ad5dnPXsZDO+sJxWWIw89ereLvFWPPd3H5o3jC0VENuslm5QT3+/4tS+jwRCbkoYHEc2nWq/GGEh7rWSaXPJuBP969lgN1Tu5YU0za987/mEatAAoYtBpuXpFPRbYZtUrkcIOLudlm7tpYgq5nAfmNqi7WlWeyZW7mkDXfvnnjokGfnY9xMhTDFdceD9Nl9BwCrgT2ApuAB6epHW97qlo8TJ8jPeGy6xVysxrUQ8oFD+QbNyzgB89XJt/3GjyQWJEfKDm6qcjMyTYP8775AjlWPdu/uJX7nzyOJMMntpQSV+BfHjlEOCZh0au5bF7WrMb/CJj0k3dtSiftSOPjrdMdKe/V/5+98w6To7yy/q+qc56co+IoZyEQOWcDBhtsHHBibX9Ou95dr43tXYc1ztnrbOMEGAMGk0VGEso5jkaTc+qcK3x/VE9P93RPHmkkrPM8gunuqurq7qr3fe+9557D6B4bAN/ZdIxATGVve4DmPh81hcPhWkxR8IclYpKa4Rl1OvDyoXbccQFZVUnV92gcDPPAmy1U59t4x9pK1tXm0e+P0jIQIhiTKXVZuGxhEdsbB/BFNJftuKLw1P5Ocq0GjvcE2NzQz2ULC0FVmVPg5KrFdm5bVc6DO1r53KMH8IXjLK9w8eGL5lDf4+fR3e0MBmPYTHretrKEDk+YY91+FpU6qMgdvd/JbDh9U8mpaNa/ZEEhcwvtmjqR8VzQM12MzBhnC3ZS0RvMLlDwq9ebuHFFKd+7eQn/+uThMY9x7yXzaRsIMkwi0WDVM+GAZ0O5mQ88tBvQrrNl5U66fFEEwGbUcaTTx+Ky0VM9Q4nUhh4/9/5pN1FJ4f0X1PChiyYnEjIdPPQ/U5Nu+/FLJ3j2UDdvX13Bx6bQM6ITBe5aX0mfPzotY9GYpOAJxSgaxVfsnxlLy3NYWp4z7eOYdAIuq4HlFU7etrKCPKuR329p4li3nzvXV/KN25ZlqLBNxBD+bMBsBT2PAn8WBOEN4K+qqp6T6ZgiZk6Ha3Iw6bSGa3dERi+CQRQnFPAAaQHPSGRpx2Br23D/Rpc3wtv/bzOekPZeOVYDn7t+EQOhKJIC7pDEzqY+Nsw9p/E/GpoHZu6qOTBjR5oc+kdcauNdeYGUSPp//nGE331gQ/Lx3lY3Lx3tQVJUDBP0EZlJdEdgNHeMI51eKnOtvFbfi8Wko9MdJi6rmA06+gNRIjGJA21eREFAAfwRiYik0OOPIMsq4ThsOtKLzaRnRWUud6ypoL7bjycUo6E3QExSiEoyPf4o/YEo7W6NKz2/yM7Kyhx+9moD4ajM8W4f/+/y+aN+hnn3PTvzX8xphCgKVJ1iEYt/Jky2lpxr1uPOknDYcrwnzURxPPxpR3prsNOs59tvX869f94zof23daSPjXaTgYvnF3C808Mr9f1sbniDXfddjcuavZIxpPy3s2WQSFwL5HY1u/nQRRP+CNPGM4d6uHXN5FsCfv5aI5Ki8oMX66cU9IBGLa/On/qyMhST+Owj++n1Rbl55dklBHE2ISqr9PpjvHK0nx5vDLNBx6FOb8LrLcqSMhdzxzF/P1sxK0GPqqpR4PbZeO9UnKO+TR1RGaKyNqgrCkjTlPqcDAb8EXoCEjqBZKO1US8ypM51uPP092WcTZjF3vRTBjMTTwBU5aRnEFVFxR2KISngCZ5Z/U6xuMKWk/04THp2tbgpsBlQUYnGFVZUuvjUQ/vo8UdxmHQYRAFZUZFjWsXTF9cWkRFJQVLitLnD/G13G3/f24GkqDjNejq9EWQFciz6hEKPC0lWWV6RQyAq0TYY4ni3nxyrkUMdU3NZnwkc/9q1yb/PVC+acxgd5nEog9kCHoAdbRO/5hbdl3lN+CIS9z15aMLHGAlFVVlekcvrJ/oBrS/wf544wPfuWjPmfjctK+XFI70MBmPcc2FNxusXf+tlur0RPnPF9FW4RuKqxSVT2m9IMj8mnxqRoomgZSDIkU4fcVnhlWOnXvr+bMO3njvKnlYPn7liwYwd02wQcVoMicSZis2kxz4D8tJnKt66n+wcZh16UaM12I06vKP4KwzBpIfoOOl6gwjFThPtHq3fR1ahtd/H8R4v37tjGV/4+xEsBvG0UgnORszN1dM+2ycxwyjNtdLknpiZ3fHe9DZzSQGjTuuw1oLnMweBmMx8m5GWwRA6QaDPr137ggCxuIwnHMekFzEbdDgtRgaCMWQVopKCzaQjEJVRAUVR8YSi/OnNVrp8YUQEVlflcNnCIgJRic0nBrhkYQHravIIRmUWljh44XAPelFAklWq86w09mvf21DAcToTQwvvey75fpHY7HhDncPUYTXqqM230DQwddWl8TAa0WDAn12N7LzaPFZV5fDz1zKFDuxGkQXFDspyLNy8soyfvDKsHFdXOr68u91i5DfvX5f1tW8+c4TWQe17+NYLGe3M08bxLi9r5kyeimTWC0QlFYth9rJi+TYjTrOBgUCUmvzZ0kA8M7G/1c2ftrWiqiqffzy7dPtEYTNo/VfLy12854JaFpc5efloD4FInCuXlFD8FqYWnlkz/Dm8JeAy69CLUOYys64mj4fuvSCjumBLSfwV2I3cujJTJcphSr88i5xmSkYYvomijlt+spUP/mEvNpOBu8+vZVllzkx9lLck6gffeovGtgkGPABG1ZP22GXRo6DRw0yG0z8kjvWOJr3IioocVEUlHJfwh2N4wxKhmMy2xkEqcy0UOEzk20xYjVoTvkEnIKBJOxt0AqIAelGgzR3GF44SlRR0ooA/GifHakBWFI50+Xh0TweHO300DwTZ1TyIUS+QazVSU2ClLNfC2upcDqZUe1KrLfu+dNWp+4JGoO5LZ6cs8z8z8u0m1lXMjuzJaByE3U2DXL+0NOtrgZjCvjYv33/nShYUO3j0o+eRY9FTmWviV5ub+NjPp+5LVZk33O+iOwXDzVRjlm+8fRkb5+XzrdtXzuj5TAYmvY6aAhsLShwJE/JzGILLakBRFGKSPO2+w1ybkflFDuYUOci3G8izGrh9TSW3r62izGUhEJFQUxrxYpKS9vhsxrlKzzlMCzoBavKtdHjCiJKK2aJjMKxVdUJxhYfuPR+Aj106lwe2NuGPKhhEiChg1otcv6yYVVV5vHNNJRctKOQLj+3DE9GmqfnFThp6A/gikrZw0wnsahumronAiZ4AkqyiqCodnhADgRiDwVhWdZFzeOtiMmHc6y3pj30RKWl+GYufPprmEAQyG79JPLeiMoeFpXZCO2QUVU060ssqdPkiXJtr5ZaVpXzh74eJyyo5NiOd7jCSoqIXEqpZKggieMNxbEY9hXYTVXlWcm0GTvT68YXjVOfbcFkM9PqiyIrK6/X9GPVatvu9F9SMK5EajsYx60UkRSHfdmrkVM8mbP3PS2f7FM4oNPUF+Ov+nvE3nAbm5BtpHJi4x4wE3PzTLaO+nno/rqkuYN+Xr2HOfz2NosIzE/dCzcC7NtRypNPHlpMDPHDPBub/burHyoaawqlVSG5dVcmtq2ZPoh7AoBdZWOIgJilU5p3rsUuFzaQj12ZiMBhjzhR/4yG0eyK0eyJsOTnAb7c0sajEQW2BjT2tHlRUCmwmzp+bz79fU8feNg+v1/dR6jJz+5qKpF/a2YpzQc8omApn/J+xD0hR4ZZV5XziigXc/8xRfrN5mCqQKv362WvqWFeTy//7yz5CMQlZBZ2qcrwnyPfeuRpVVTnR60ev1yMQQy8KrK7O4bGPbeQHLx7DF5J5fYThqMkgUJ5nwRuV8ITjrK/JY2GJg2LHW7c0OxO4cG4ej8zQsRbMkmZ1hXPmFLbKcy1U5FoIRGSWlJ/+D+TQQ0DReuNSodcJOMw6jnT6sRh1GPUi/ugwPchuFNGJAlsaBohKMioqvnAcBe2+1OsFVAREQcWgEynPsWA16ilwGLl+aRkvH++hzx8hJitcMCefDXPz6agM0+WNsDnRwxCOj+4JkTreySoYdAIgYJwFMYgzDRd889V/yvlgNMSVyWeJC20G+ibRY3fL6hree341K7+SKZ1+Ua2FN5qmT62bqSv7a7etmKEjZeLKr73CjrP02rMa9dy5rpK+QJR5b9FG+qmiPxBHRRNv6vHNjMG4CkiySrsnTCQuIykKnlAcu1HPsW4/objMiR4t0dzljeCLSGe9Mu7ZHbKdw6yh2CqQbzOwrNzBJxJNdTk2A2aDRm0rspt48dMb0/ZZV5VHscuE3azHqBOwGHR8ONHkKQgC59Xmc/H8AvJsBiryrLzvfM3c7NNX1vGlm5fw8UvnYjGICIDTpGNekYNFZS62fO4KDv/Ptfzm/eu5flnphHxF/pnxnyN8TT60YZQNJ4C/fuqKKe/77VvrprzvA+9dnfb4vauz01SyYfOn07n2Nfk27tlYy7s3VHHb6opR9poa7CNEnkQyhSSW1RRQlWfFZtShF7TqqU7QaDDXLy3FatSzojKHjfMKuHZJEVaDSJnTzOdvXMqd6yrJt5uozrORbzNx7dISbEY9dpOO8jwbdaVOFpW5+MCFtXzhhsUsKHYwv9BBXamDxaVOTHqRmnwbK6tyEASBilwra6pyWVTqwGUxZBgVLit30Xz/DRkL+iKnhQvmFVBdYOOW1eUz+h2OxLlgYnQ0feP62T4FAD560fR8Wprvv4GdX7yalz8ycQ+md66rJMdqpPn+G/i3K2rTXvvjvZfTfP8NPPjhDVRnSZKLaEHW7vvGlnv+4TtXUjwiB5CaYnvuU6dRpm0UnK0BzxDy7SbqSpxnfUVhprGw2M6Vi4qoyrPxgQtrsUwzuWQSNelqq0nH2uo8rlhcTK7VyMISByU5Fi6rK8Ru0rO2JhenxcDiMie5o6gWnk0Qzgae3tq1a9Vdu3b90yn2TGRyd1QsIP/u75NngT99aCOf/ut+LplfwBduXJLcpu5zTyeVrQSg2GHkrvWV/OL1Ji6Yl8uv3zf2qjcUk2jsC1KZa01KdTb0+Dnc5ePyuiIc5uEb4Vevn8QbjvPpK+ah12cWEt3BGB2eMPOK7OMq+oyFQx1emvuDXLmoCPMo5nXl8xZjuP3bAFh1kOc0EwxHCMe0BWVlvo0lpS6cVs0Y0mwQ+crblqSddzQu8+LRXirzLBTYTXjDcZxmPbtb3FiNeiJxmauXlCQb4F862oNJL3JhQtP+UIeX5w91s6TcybVLS4nFZH6/rYnBQIxL64rIsRqxm/RJ9+JgVEIUBPSigE4UyLcZ6fRGmFdkR5El7v3TXqJxGYNeZF1NHresqkBSFOYVOXhsdxt/3tZCTJK5/+0rGQzHeHxPO0adyEcvm4usaJmduYV2TKXzKX3fD4Ds19nV33sVXzjOi586H7s9PePW3B/g1280cf2yEi7IYtb35ScO0u2N8uM7l2M0pmeF/u/lY3znhZMsLbPzxCcvydh3/n89TVyFxz62mtVV6YHMQCDKQztbOa8mj7W1mZLke1vddHkiXL24GP0IQYKYpFDf46fYaabQMTb9qrZuGeKt9zMkYpRgiAGwsMjKt25fyRsNfXz/hRPIgAGQgcsXFHDN8lKuXlKKK8Wcr20gxO5WN+trchkMxSl1mbEa9ZzsC1DqMtPUHyQQlVhdnYvTnD6pqKpKfU8Aq1E3Kt1j5GcbDMbY0+Km2GliabkrzW/hZF8AAZhTaCcqyZzoCVDiMk/Y4Xvt2rXs2rUr62uDwRido9zbQ2P324AfnuWLstEw3j11uuANx2kbDDGn0DZhY89TjeI5i7C84ztY9XDbmioWlTjwRyXed0Ftsj8hdX4f7/sb2vb+G+q486K50zo3WVGp7/GTYzVMiRYtSQovHOmhNMfMqqrJu+adKdfNmYhz383oWLt2Lf1X/k/y8U/vWs4NKzSfpB5fhAXFjjNOmOd0QRCE3aqqrs362tkQ9BQUFKg1NTVpz0XjCjFZxmE2oAK9vgj+iIQgQI7FgN1swDSLP7isqASjEjqdgO0UTjzNzc2M/G7OQcM/63fjDsaIyQpRSSEmK5j1IgadmCZFWX+yEZ2zCJfZQMGIIEBVoT+g9XYUOkxJWfBUxGQFgygiTDLZ5AvHCcdlLaCzm2aMLjKTOHriJDpnEaIgYDXqcJgNOMyj38OSohKTFPQ6LVDVTfZLOYsw2j2lAs39QaKSQo7FkOHqPhiMEZcVCh0mxDPw+1FViEgyBlFEr5va+TU2NWPJK8ZhMmAf43o51ZAT16PJIM7ad62oKk39QeKySr7dSLC/K+26icsKkqKmUaBjkkI4LmM26CY0d8ckhb6ARvMptJsmtMCTEvOyw6RHAXp8EVQVip3mBD3z9OPoiZMIjiIMOoE5BfZJj6mgfRe9/ih5NsMZE+jOBI7Un0S1F2DQicwvckzpu3mrorm5mfySCgJRCZdZjyAImA06BAFCMZlwXMag09Q3DToBh9mAokIoKmFKCN4oqspAIIaivrWkqnfv3q2qqpp1QDgrPmFNTQ1vbN3OUwc6iUoKc4tsfOIvewkFYkRNOhYUOxAGglgSDcklTjM3rijjphVlzCuaOC9UkhQeeLOZqKy5KE9n8HjmYBevH+/DZtJz5/pKDnf6MBt0XLGoKNk0PVUcbPfij8RZU5PLxg3njZp5/WfHWFnptxpO9vr5n38cZnvjILKsogMcguaPYdCJ5NmMFDqM/PI9a3FZjVjK5pN313fRi/DqfVditw4HPo/ubuM7L9SjqCq3rSrPoMP9fmsTb54coDrPyueuW5RGJ/QEY7zvd9vxR2W+ffsy1lSnV2R++FI9B9u95NmM/M/NS9NUaCJxmZ++0oA7GONDF9VSUzA7nG5L2QKK3v09VMBmEKgusHP9slLmFzu4dGEhJr0OWVF5+VgvA4Eov9vSRK8/ikknUlto5wMba7lqSfGsnPupxpIVq/jYDx5hXpGdixcMV/mOd3u59gebUQGzQWDXV4dpVn96s4kvPnEEHbCgNpeH7r3g9J/4OHjuUDdHu3zoRYF7Lqyd0uRvLVuA8+3fQhLghS9cRZ799HPfVVXl1280EYhKFDvNvOu8yZtUzgSe3NfOJx/aD4DTrKPwqfuSY3G3N8T/+8s+BoJRLAYdcwrt/Od1dfxwUz3bGwdYXObk/+5eOy5N+ddvNPK7LU0A3LOxdkJWBW/7yWb6A1Gq8qzctKKcJ/ZpZqbXLi3hno21Wffp9IQx6MSMCnF/IMK9f9xDXJb50Z2rpjxemUvnU3L39wD45b0bWJOlij0eln35OaxRGUkvsOWLV2Iynd19F0Mwlc6n9L0/AODfbq7jXRdMr6r3VsLSFasIX/81DCpIosD1y0pQBYF3n1fNtsaBZAVzfpGd7Y2D+KNxFFXFYtCTbzfy47tW4Y9K/HlbKwBzCm28beWppSafLgiCMKob8VlT+zrZF+DRPW38bVcrrx3rxR2MIQP+qMzJvgD+qIRRp2WehwanmDQ5JaZnDnfxwpEeXjvexyO70p1MdjUP8sS+Dnr9E7NA3NY4wBsNfbx0rIedzYM09Qc52uWjoXdisi8n+wK8eKQn6csxhLbBEC8e7WF70yBvnhyY2Ac7h7csXjzcxT2/3c47fvEmb5wYIJpiLCerQ7LFWh9JXFbZ2TwIaI3uoHnUBEb4nvT5I7iDUTzBGJ3ezObfrQ0DdLjD7Gl1Exyx7/dePM7hTh+tA0H++8kjGfvqBAGDTmvAV0mvMu9oGmRH0yAnegM8vq9zSt/HaIhJCoc7vRn3Uzao6vCZBeMqDb0BHt3dzuEOL4c6fAA09QfZerKf1+r7aBkI4Y/E6fFHOdzp5blDXcjjNG63DoR4Yl8Hhztnz/BzKvCF4/x1ZyuP7GpNOs4DdHlDye8sGk//7H94szn52q4W9yk/x31tHl453ktoEn4+MVmbK2RVRZ6iOaOcYE1IKrzZcGqVykaDqg5/lrh8+pUIh7CzaXhu8kfSPdr2tno50eOjfTBEhztMnz/KG/X9PHWwiw5vhJeP96Gq45+7xahVr20m/YSk5mVZZjCoqbv1+aMsr3Alq0O+cJwtDf0ZsryHO708vLONP29vodOTPhZ++7ljHOn0cqInwP8+c2zc9x8Nqe/44tGuKR0jFFdQ0JrS3ZGJK9idTXjq0MzOCWc7enyRJAU7pqi8Vt/H3lY3mw53UZWQRa9OKOvW9/ppHQzR1B/CHYwSjEqE4jJFDjMXzi9gfrGdi+ZP3ttpphCTFN440cf2xgGUKYieTAZnRaUH4Il9HexscmvZV6OOQoeJXl8UQQCzXkdlrpW5RTbW1eQxp9CB2SCyqNQxqfcoSJFazU9RqBgMxnhjyJFZVrl9zfjNzkOZIZ0oYDfp6RWiGHQiRYmArMsbZn+bh3lFduYVpZ9nVJJ5+oC2cOr2Rbh7Q3XyNaNeC+xUVdO0H8Jo/U7neLBvLXR7Q7x6vB+bSY+iqNz/7DHcoVjWRbYOMOkF5hc50OlEbEYdyytygGEVIhFwmtL7SGwmA0a9DkVRcWTJeFuMIu6QJgtuGlG1zLUaURFQVTXjNYCyHAtHu3zk2Yxp1y9AVZ4Vi0FHVFKoLZhZY7oXj/ZwvNuPUS9yz8aJV3H1AuTbjaiAKAgUJnpflEQWLRpXyLMZcYdi6HWaSlpZjjkrJTAVLx3rwROK09QfZEGxY9rV39OFgUCM+t4Ajf1B/vumKGaD1me0qrIAk14gJqmUjqC21eRZqO/VfJQsp9gHqcMTTjq5S7LKVYsnVnG7oq6IfJuREpc52bc4HZTPkseIKArcuqqcxr7gpOe/mUR5Sm/MyDvBE4zii2imuZG4jM2kY31NLnpRII6WGFHV7PePJCt4w3FyrUZuXVWBJ6T5ibx9HAESSVZoHghx1/oqtjUNcPOKMpaWu/jJXat48WgPrYNhdjQNUpVnTeud84Y09ThV1XqlUr1jChxmFFULWvKsM7OUcpmmdn+sr85hX7uXihwLBfa3ptTzgqLZu57PRIwUevBFJPxRidfq+/m3axayp8XD9qYBci0Gip0mOtwRXBYd84sd3La6ImktsK4mbzZOPw17W93satYSYk6LgUWlp05F9awJevyRuFbuVjXjsM3/cSlP7u9iXpGdbY0D1PcEaHeH2dzQj6TA+y6omfR7XDCvAItRR0xSOG/OcInZatThC8dpd4cmPJHcfV4VqqpSnmPh5pXleMNxDDohudh67mA37Z4wR7v8/L/LbWmLHp0gYDaIBKNyBs2i2GnmjrWVBCIS8ydB3TuHsxcnuv08fbALTzjGnlYP7mCMqjwrNflWDHoBnQg2k56lZU7mFVooz7NztNvPq8f6MOpFilxmvnfHCkRx+BqzGHU4TDqKnWb0hvRrrKbAhsOsR5IV5meZaHp9USRFxROOEZdVjClrxHeuq2J/u4dQROZTVy/I2DcUkyjLsSAKAjFJSaO31RTY+Ppty/CF49SVzOwEF5W0bLMkq+NWYYYgAP961TwiMlTlWrm8roi8RNBjNuhYUZFDXFaYW2QnFI0TlRTWVOXxkUvGp9k4zJokaE2+Ff1ZpjaoEwUEQSAUUxkaJa0mHbesLOdwh5fb1qRTJN6xvpqX6weQFLhuackpPTeLQYdOFJAVFZtp4kIpNpOejfMKpvXeQ0G+1Sgyp9g1rWNNB7KiEpPlCV/npwLmlHnLOuJ3CEtK0pSzyGnkG7cuo9hl4fw5uWxrdFOdb+W1E32sqc4lx5pO03p0TzudnggLSxxcv6yUj182b0Ln8+LRXo52aRTzX75nbVJow2U1Up1vo3UwjFEvZgS8q6tziUgyJr2OhcXpY9Idayrp8UaIywrv2zj+PT8R1BROXggB4MYV5eTaTSwsdrxlVc+WVkztu3mrothpRrTq8YQkDDpA0HpKjXqBV4/34Q7GAQGb2cD/3rqMv+5qw6AXWVTi5LplE1c7PR2wpYwXp7IHHs6SoCcqKdTkWajKs+KPxFlc4mDTkR6MBhG72UBNgR13SOKZg11Iikpzf3BKQQ+QVF853OklENEUlbzhOEe7fEQkhZ3Ng1w7iotzKoqcZi5ZWJSsGKWqOQE0D4Q41u0j32bM4BjqdSJ3rq+ixxuhOj8z4z1bWcRzOD0IheN8/KHdHO32U+K0MOiP0B/Ugn6dKCKg4o9IXFZXRInLzL42L9X5Vs6bk8/ldcM9Yz995QSdnjD/etXCtIAHtEpFJC4TiEkZEsq93jC9iQbf5v5QxvmZ9CJGnYhJLyYpPUMoy7Fw78VzcYfibMjCTZcVaBkIUZZjydo4XJ5jGfX6lmSFlsEQhQ5ThsrZeLhyUTH72jyU5VjS1AbHggoc6vLhCUq8fryXf+zv5J6NtVxaV0RVvpXrl5XS44uwp9WN3mmmyGni3edVj3tc0Mr5NqPWG6SomkT16UYoJnG400fZGN/5SJTmmKktsHHpggIqU8amYFTiuUNdBKIyj+xs4wMXDnPvD7T7GGIa72+bGXqbNxynwx1mTqEtTSkuz2bkrvVV+CJx5sxwtXA8DNHJQjEFdzCK03L65V216u9RWgZCLK908b+3Lj/t5wBw04pyvvP8MYIxlasWFvD60xplu8hh4p6NtTxzsIvmgRDrq/N4cGcbt64q52CHn4ikcLTLz/c2HefyumL+7eqFyWPKikqXV6OXd7g1qtmbDRoD4/xxAtZAVKM6RiVZE3dJuWbW1uRRmWfN2shtNui4vK447TitAyFqCqwU2E2YDDpEUZgxb7gi59R6ccJxmflFjoTvivKWDHyc5rdGn9JMIRqXkaNSgtYIBXY9wZhCMCbz5knNu81hNlCVZ2VRiYPKPBsxSSEYlcc99unG0nIXDrM+wZQ4tevbWQ16BEH4EHClqqp3jrVdtzfCn7a14I3IhKMyD+5q56Fd7eTbjTjNBn7yrlW09geIySqqqtLuieANxdjb5mFlZU4yW3Sk08uft7cyp8DGB8doenzxSA8PvNmMDugPRtk4N5+WgRDBmETBSOONUfDGiT6OdmmmToUOE8XO9EGxIteCiorDrNeMOkfs7zQbJr2wO4ezG7FYjM89dojH93Uled493hhWg4iK1mti0ovkWg1cOC+fKxeXoKrw0tFeurwaN77TEyYak/j2C/UAvP/8al6v7+em5aVpMs4RSSGuQLc3ii8UIc8xTIl4fE8H8cQi9dlDnXz5bcPy5wC5NiPhTh/FTlNapQa0XpWnD3QRV1RcZgMb56cvRjyhGKGYhD8SJ5Zlct58oo/mvgB3X5DZVLzpSA/Huv1YjTrev7Emgx43Fhxmw4Q5y6lh3KbDvQgCDLWvNPUH+aqwFJtJjy8ssa/NgwjkWvW0D4Z45kAn1y8vG/c9FFU7J0EQEn0EmVGPkuBp+6MSlywozEicTBcvHO6hqT+IXhT40EVzMn7LbMi1GnnhM5lS48e6vHgTvRvHe4Npr+1pHe7vaOqbuEHkXb/YyrYmNw6Tjv3/fU1SeltRVH7+6kn2t7m5ekkx7x+RZS90mMaVJT8VSO1C6fFFqZ4FIY64rLC31Y0/KhGNT7ynaabx3L42AjHtTvrHoR7kYIxvPnuMEpeZO9aUoyKQYzHQ5glTkW/HHYrT748iJW6+lv4gb9T3pQU9OlHgoR2t9PiiLCyxk2sz8PWnjwLw+esXMb/Ywa7mQepKnFw4v4A/vtlMnz/KRy+dxxV1RexucVORZ8k6r46cn0fDI7va8ITiFDhMWPRiokdX5Xdbm3j/BTXkT1D6fTQ8tbed1TWTFzKozrPw193tXLqg6C0Z8ABsOtzJ1RNIOP+zwB+R0CfmJRnoDcSxGnV0eSK8cLibroSBqdOsA0HgpuVlHOv20euP8pvNTVxeVzTjNPLpIFuC/1Rg1oIeQRBE4D3AhDr32r3pzXkqEInJmPQioajE3efV8KstzfgjcUqdJj78h11EJYXKPCs/eZdmZPinbS2c7AvS0Btg47wC6lJ4g6GYhIBAlzfMn7Y1c7zLS29A4/Me6/YnS26e0MQmEotRowcZ9GKG/KaiqFy5uIh9bR7mF/3zaqmfgwZPMMznHzvM80d6GNlDLQAOiwFFVVlQaKMvGOeyBYXkO0z8+9/28+bJfiJRmaisohMFopLMK8d6k8f5/bYW1lb7icky71yXXcmpsc+fFvT0B4ab/UeKHACc6Akks67+cDxJ+QI41u3l2UPdyKrWDzQy6GnsC9A6GCIQlTLoNw/vbOULjx9CUVWeOdzDXz6c7h/lj2jnEo7LxGWV06GuGR/RT93pCfO7LU1U59l48kAnAjCvyI4/IjEQjPLUgS5ybEYumDt25vnG5aUc6fJRW2AbdZHS2B9kX5sH0PoYr1h0ahThJisDmy2TbDMakn5GIyW797cOV3diWRhXgaiEXhQyvH3ebNL280Vlfre5kQ8k/Fgi0Si/eqMRSVHZ1+7NCHrOBBzqGGD9nMkvXqcLk0FHtzeKAkRimVXa04W/7h1uOpcUCEclvOE4vnCcbm+Ubm+EcCxOtzdCpzdCc38wGfCAVtWRRowP3Z4I3YmF3LHuAA9sacKd6Ll5YGsTN68sJxiV2dk8SH8gws9fOwmAOxTjq7cs48os/V1/2tpEXGVU5baRiCQGhEhMZmmZE50o4AlJtA2G+Mv2Vt69oXpajvUvHevgS6yY9H6P7+vEF5Z49lAXd6ytfEuuKTYd7p7tUzijEIhK5KQ8VoFgTCbfZkgTGPJFZLrcIQodRnY2S2w+0UtVvp09Le60oEeb08MU2E3T8lA83ZhsZXM2Kz3vAF4Eloy34WifpzTHxMY5BaytzWd3i5uaAiuRmMz62jw2HelFVlScFgOKoiCKIrUFNk72BbGb9Gk+Em2DIR7f24EnpJnrvVbfn5btVRRt4amq6oQj4zyrAV9EotBhTLuABgJRHtndjqKq3L66AhX4zRuNLC13pfURHWj30NgXZE117qimhOdwdqPTE+IzD+9je1N2yo/VIHLjslK2NbtxmkQ2N2rbtQ6G+Phl82gdCBGNK5iNOoqtBjbUFtDqDuKyGOgPaosBnQB7Wt0MBqKjBj3lIwz5clIqCtkmz3ZPCE9Iq9aYRtDmurwR4rKMopJV6bDXHyEQkUDVBqtUDMlsghYcjcQVi7RsbVW+NaukcFTSgqGZ9BrQoWXwh8YDRYVWd4hgTE4uuPa2epKVuAKHiWB0/MRIjnX8wCjfZsSoF4lJyoQz0ZPB1UuKOdrlo9RlmVCVB7SJ9scvN1BbYONtK8uS1ZdllTncsryI1xvcfOH6hWn7xMdgUzT0Bnj6QBcGvcCd66pGXTCWuoYD68Gw1rwukKlQpqoqmxv6cYfiXDy/IKMn5HQhEInPyvv2eiPJitMkxUtnFBtrbOzv8CcfO8x6Ch0mqvIsVOVbybHq8YQ076Yeb5QtJ/vT9s+1Glhalt7MXOwyJQNrvU4gkvIBo5LCj18+gT8iY9TBd+9YOe45fvmJg/xpexugqc199x2rxr0Pbl5ZRn2Pn0UlTkpcZr5x2zJeO95LfY8fXzRGMCpNK+gxTNGIxmbUM0AMs0HHWzDeAWAWra/OSMhK9ht8IBhPWzPbTTquXVrK5x8/yAuHe4hKCsVOH1e+c2Xafs8e6uJET4Bcq4H3nl8zrmR8NvT4ImxrHKA8x8LaUyyQICsqj+5pp8Md5uIFBaypntj7zcplJAiCEa3K8ynga6Ns8xHgIwDWvGKGpnwByLUZ8IXinOgNcaK3lVtXl/N6fR/+sEwkLtPjjbK2Jpd+f4yPXDwn2c/w4YvnsnFeAW+c6Od7m+q5c30li0tdNPQGONThJRCNa3STEefyjjXlrKstQK+DKxdPrBG3aSBEns2IrGiZ84pcLXBpHggRjmmrgMb+IE/u6+BEb4AXjvTw03evosBuJhKXeflYL6oKvkic21ZX0NwfpDrfOuF+hHM4c/HnbU384rUmevwRolKW1Dcwv8DKps9exr5WD8f7AnR7hilDkbhCZa6Fbm+EfJuRKxcXc8WiYoqdJh7Y2ky3N8ycAitLy3N4Yn8XgahEly/C9pMDnDc3M/vcG4pSmvJ0PKVPR8xCuxqqdkoKdPqCzLfmJF/LsRqISSqymm48OARJVlFkBVmRMxSablpZwtP7u5BVlXU1mU2r+XYTVy/Jfv/5I3Ee3NFKKCZz1eJilpTNTCO5TtQU7oYqPnpRq2Sc6Ble0EmKitkgoiKwcW4eV01wjBgPuTYj77ughmhcnjZtJhusRv2EJ4ohROIy0bhmExCVhnsjBn1hHj+gqaZ94e+HuW3tcG/T3EIbh7u163fk1XSs28fBDg8GnciF80KjLhgf2tnKdcs1ha6KPDvra/M51OHJ6K9sd2vqbVFJQS8IXL98dugwS8tmp+k6z2Ykx6LHH5EyDGJPJ/64Mz0rn2M18sUbF5NjNdDtjSTtJFRVRRQELCOoqvOL7CwpT7+HVRX+5ZK57Gtzc8XCAlxWE1968jAAH7moln9/9BAAMRnmFlu59+I59AZifPzSeXR4wmxt6Kci18r5iTHwcKcfVdV66l440sMtP93Mb9+/nvLc0XsKUnsOu31hvvrUEQ60uRkMSeh1AjcuK5tWktIfm1qk+oUbFrG1YYAVVa6M/s23CmYpj3DGQjfG7zyUC3rXujI+dOE8njrUyZaGfqKSgihogi9Vies0GJV46Vgvb5zoo9hhwhOOE1cUTKJ2T4ZjsqaSPIHqz2v1fXS4wzT2BZlbaCd3GgmA8RCISMnevmPd/jM76AH+C/gJMGpKVFXVXwK/BFi+crVqzzETjsUpd1kQBIHB4PAd8Ns3mnjPxhr+uqsdWZI52OGmaUDLMr9a38cP3rmCn7zcQKnLzCevmM/LCUnTP25t4RtvX44gaBQPh0nPxQuK2NIwzEE/v8qO0aCnNaHclm0hlw1rq3PpdIc51Ollc0M/dyay7AuK7Rzr9qGoUFfi4O+qFh2DQENvkAK7GYNOJNdqZDAYo8hh4rE97QwEYuRYDRMuw5/DmYV4XGLD/S8zEJzYyN2V8JPZ2+am1GnGqhPwR3yE4wp1JXa2Ng6SbzeiEwXee34NRU4zhzq8gIDFoKc0x0Kxy0JFroWTfQGcZgMLS7JXKW3G9IGp3GVkd+LvbJlPURj2+Rm5jPUEY0mvE08o0xNnf7uHmAyeqIwgpAd8DqORfIcJSVapyJtcM2N/IJZs0GwbDM1Y0DNyDSIp2mdPdbpXgXAiKnpsbxc3LO/EE1IodJi4aH5BshoyFdjPQJfsw50+5hbZ02i7jydMHgHCIwL5Qd9wwD4yxA/HZDo9EYx6EWkMf5x7L5yf9vida8sx6ATuWJuuFCcpCse7A0iKMilj6kMdHr7zfD0rK3P49FWZqoOThX2WzCH1epGl5S52NQ+yYc7sSdFa9OAfYRczVK3c1+rmZG8QBbAZRS6cX8j1S4v51MMHktsGYzKeUPpYKYoC772gmo29BViNIt6IxH9eW4deJ3L10rJk0APw9P4ebl9TwU0/fo2fvNwAwNwCAzeuqGFxqROX1cAP3rmcO36xjYFADJdZJBCV2NU8yGv1cV443MO/X1OXEXil4g9bm9neOJgUSRAFzZR8ReXUA95cy9TGipVf2ZT8+61qUyGcC3rS4LKMPy+8dLSfQ10BBARyrEbikorDoudDF9VSkmB4HGj3crI3gN2ox2TQcUVdcbJftsMT5rHd7QgC3L6mctxESrHTTIc7jMOsz1BtnGk4LXoWlzlpHQixpnri99xspQRuQKvyPABcIgjCvWNtbNSLbP3cFSiqwKGuAAc7/Wmvv2NVIb94rVFTkwLa3Om0mm8/tZ9Ob4RdLW52NA8kpUwrEpHuvCI7hXYTOxoH+MLfD6EXodypZ12lnTmledT3+glE4mxrHORHL59gb2t2OlIqynIsBKISff4oj+/pYFfCFNJhNvDu86pZVeniq08dYV6RjQXFDjbOy8edME7TiQJ3rq/kzvWVXL24hGhiQTVZs9VzmH3E4zLrvvIC87/4/JgBT3mOGUPKfHfJ/AJUVaXbG6HLG8YXk/nP6+rY9JmLefbTl2DWi/T6I7QNhmga0O6HEpcZp8XA4jIna2pyaRkIEoxJzCu08/jHNpJjyz5gHWhJp5YMeVIBtLszKWr2RCAkAmU56VnN470BJFkz92zqz2xaj6VQnR7Z2Zz2Wq8/ijsYwx2K0ZnlfSVZoaHXjzec+T1W5VkJRuMc7faxOIusfK8vzHdeOM4rx6ZnGmk2iuRZDFgMIma9kDVr9IkH9/H84W5+t6WJx/a0JxdFZwPCMZlXj/eyP9FLNBICmoyvy2IgmjIenTdndJGIrjFaS2KyklQPHElVy02RD85xDP8dCsX5j0cPsqWhnw/8flfaPmaDjuUVLpaVu5KZzIngs48cYHfLIL/d0sT2GTB9/vITe6d9jKnAF4ywuWGAiKTy6J7ZM3OsLhj9u998oi9JwYvENU+ukVPb0nIXBVnEKEpdFupKHfx9byd/39tBqcvM3RuqcVkMXF+Xi8uiUVIf39fBbT/bQiAl8DrZHyfHakguxiry7Lz5X1fyzbcvRxD1OMwGFhTZ+fITR3jjRD/v/vW2MT+jKGr9hSpaMC/LKh++qGbc72YslOROP1lz/z/2T/sYZyJOva3x2YXRvIcdKfmWmKLiDsbRiyKFdhNvX1PBT961mjvXD1fiS11mREEgz2bkjjWVLE6hlXa4w0iKSlxW6fCML0Jz8fwC7lpfxd0bqiclNDQVCILANUtK+PDFc6grmbivz6ykEFVVXQ8gCEINcL+qqr+YyH7ZFv0WvcAn/naUwQTlxmkEvSgipfAd51cUsrWhH70oYtbr+M7tK2n3hJKc4YpcKwZRwJ1QH5IU6PJLrKq2k2PVGnR7fFHMRhGXxcDRLn9S2nosDE3aOlFI65MA+Oif9uAJxXj5WC9fvmExEVlJ40Ca9DpKE5H421aWcazbz8IZ9i45h1MHRVH4zF/28MShCS6yVTVN/enpQz0YH9pLRFJoGQhj0otsbRhg47xCvvb0Eep7/CiKSr7NyHOHejmvtpACu4kPXlibdGX/2csn6PFpprg/eaWeDXMKKXSYWF+bngFeP2LBajHqcSe4BNmGraGqhipAly/MvBTK5dx8m9YYr0JxzthZoRUJo9QhbGnoTS6kd7ZkTnH/92oDzx3uoSzHzE/etTptUN12sp8n93ciySo/fvkk374jvRn4Px49yIkeP0/u66C2wEbNFJW1YpJCXzCKOxQnKqkZlQsAp1FPU3+QSFxif7sFq1F/xvkijIbNDf2JiiEUOEwZUtZ2s4FQTOLCOflpdIfIWI07Y8Bq0GneOrpMIYNY4pgCEEkpuYVkBVlVE/K86b9AqcvCTSvK8IRirJ5E9m+oaiUKYDNPf7Kuypkd76XIKHTZ0415RS52tmaPdmtT7j0RON7tJz7id/zztlY+eUWmB8/vtjTx4pGehOeOiDcco9BpZmVFDlctr8RotvD84W76/NGMYzqMcOOysgwj4BO9gWSv7qEuL0ri2hq5/0j0e0JpYgsy8PWnj/H9O1ePud9YON7lmfK+Q/jQpXXTPsY5nPmQFCXrAl4URT54YTXPH+qi1x9BL6hcNL+CuzdUEZVVcq0Gtp7sp8hhZl6RnZoCG/dcWIMoCBmsgiVlTtrdIQRgIBjloR2tXLSgcFSLA0EQZpVWOxHMKvlTVdXm8eSqU3HT8kyufFhSkwEPaNz7Ekc6tWBegYXrl5XyjrWVLChxkmc3srwiJ8l97Q9EqMizksrkUVTYenKAOfk2FpU60YmaclNcUVhVlTOh871nYw0fvXQu9924iHkjjM2URN+EClxaV8Cd66uyyqz+21/38Y5fvMmmI92npJn5HGYW4XCY9/7qTeZ9/tkJBTw5RlhTlYMnLDGSSfbE/i56A1FEQTM8jCsqJ3v9HGz34g3H8YQkOjxhGvsCPH+4G1lRMRt0WIw62gZDdHujyKqWQe/1x6jv8bOloZ9ub3oVxT1CkbDQMTygZbNGsJv1CIBRFMjPUj0aWu7px1n31felV2xXVeVhEDXxhYVZTFFfOdZHU1+AfS1u2gfTF1T72gbp9ccYDMXZ2tCXse/Q4lhRU6l5k4eqagv1oexuNlQV2LEadOTZjNhNhgmLBJwJsCXOVScKWam8rYMhHtjazL/+7QDx+HDF7bUjHRnbTgTluVbKc8xU51vJGWEMaU6ciyCAJcWpvsBh4uqFhRh0Ah/ZWJFxzMVlTi6YVzApBaKf372aixcU8tlrFrK0PGdKnyUVsnr6JbMBHJYzw8tk2xjVsuWVOcnFms2sp20wxEAgnQobV+GHCVraEGKSwnOHuvGEYoTjMjFZpcMd5om9HRzq9HKo009DbwCrUY/NOHy9GACXCBFZ4IrvvUq/P33sMOgEWgaC9PkjrKnK5YblpZS6LHzh+rGDh11tvozniqY5R3cFx98mG85L9EDm6si4jyaKg+1eHtrRypHOzM91DmcGtjT08/DOVtoGQ3hD2Zkj3ojC7zY30eGJEJPBG5YYDMZQESjPsfDKsT62Nw7y1IHOJLvIaTZkpVHbTHpuW13BhfMLOdzho8sb4c0ZqITPJs4ssvgYeGBrI1FJoTzHTJcnwmhEr9IcK33+9AH01foBfvW+dfjCcZaUZZbBPvz7XbQMBnGah1WvBCCuqBQ7TQiii6cOdFJX4qQ2EQRNBKIocunCoqyvffv25fxmcxPXLi0ZlXYkyzLbmwZRVZU3GvqzbnMOZw7+89F9PLxz9MXf2monsiLQ2BdIepoIOpHWAU1R0KAzYNQJNA5oZWRBgAK7CZtRR4nLytvXlDO/0MHf9nTQ2BdAURWikkCHO8zBdg/luRbmFNiwGvVsa+xDJ4KqaAPX29dUcLjDh8kgYh8hg6PTp99NntAwJ0RWMvMiF88v5I0TvVTl23CMOFafP4YoCCiCOuqgPITq3HQKzB1rq2jrD9A0GOYbt2XKtu5t01TSQnGFwVCEuQwHRs0pVLpAFvO1r71tMT98uYHzavOYUzh1/xRRgMvqipGOdCf7EkYGP/U9fm5dXcHF8wuoyLUydxrvd7px/tx8ipxmnBZ9VlGBmCSjqCq+cIzG/hALSzU6ztLKHKB10u93vMfLq8f7MOhF3rW+Ks2rYaj6IqggpuTn/H4/zx7VAtsfvdbKv163bNLvOxIvHOnBH5HY3jjIzSvKp20surh0dvppRioizhYGg7FRX3vhUEeymdcTlnBaDESyeArpRqhHGfUi84rsHO70YjPqiMQkWsNxenwdVOZZae4PYjPpKXaaKLAa2do4gILm29QfjCHJKkFFYkeTm+uXD489J/sCxCQFRVXp8Ib50V0Tq9TUlbg42RdK3v86gQmvDQ619vNSfT+fujI9sJqqBEJVvg0EYVrefq8c1xRvB0O9aRSnczgz4AnFeOVYb3J+Hit3lzoKyIBeFNAn7qchU3BREBAETQVt5L02Ek6LHpfFgDccnxRt+EzEWRH09Pqj3P/scWKSkuFjMgSjAMsqnCDqiEsK/pSFz7JyF0/sbmNNTQ56XU7afpFIjIOdPq2knXJsvQ5WV+Wwq9XD/GI70biCOxRLqrBNFxfMK+SCeaPz4Ht9Ef5xoIscqwF3MM76Uyz/dw7Tw7IvPo1/jDV+bb4VWREQBYHza3N5vWGQUFzBHVYAhXmFNq5YVEynJ4w/KuEJx1lXk0eR3YQ7LHBFXSErK3NQVbhxaQmBcJyT/QFiCclqSVF57mA3vmiUV470cenCAuYV2xnwx/jMVfPJs2gqbzVZ5J5dI5qu60qctCR6agocmQvfQFQi12IEVesBcViGF6QXLcznj9uakBU4b06mJHOuRcQdVjR/m+KctNdeOd7DT15tQgGMOpHvvnNV2uupt/7Lh3tZVzN8/1y3tIS/7+9EUVRWVacfF6BpIExVng1fRCISl6fsQ6AXBWRF5aL5hcmMs8Oko9M3/OOH4zK5VgOX1Z0aX51TCUEQxhQAMOhEzVjVpGdu4XDQefWyCowcIAZsqEyv0tXmGWkazL4I/uObrXgS/kt/erM5rQm806slr2SgddDPwsSCMlUKeaZQ36Op0fX5o/QHotMOejbMH1uO/FRBPkP6Ps0GHd5Ydsrj0wfTK+B6UcRqNGAQNaaGACwstvOd2zMTH5++Yh4/eLGegx0+ZEVFBFxWkR5vhHeuq+T5w92cN6eCyhwLJweChGIyNYU2FFWlNxDDbBAzxiW9TsQgahLo4y3+UvGTd69GfHAPb5zoz+hHGwtbT/Txrt/sAOBXbzSnvTbVOt3FCwo51OGlItcyqc+QiopcCy0DoRlb48w0zowa5uwhEpd4eFcb0bhMQ19gQswfu1GkOt/Cey6oTiqpXVZXRFmOBYNO4K+72ojLKreuKqdsFMoaaO0W7zm/mlBUxjXFSuKZgrMi6IlLCvExAh4Ao07TJ79uWT7ukJ3L6wrZ3jjIYDDG4/uGGzpvXl7Cj961BtB46K839CUXMlazHh1ahWdhsZ3lFTmowD/2dRKTFfa0uGl3h/lYfO64CxpVVWkbDOOyGCZ0kXzz6cNcv7KUZeVacHO0248vHOeKumKurCtk2TQUYc7h1GHziV7u/s3OrK/l2w3EJZVCu5FbVpbx+L5Oun0RmvQ6xBHKZb6IRCAco7EvwEAgjgLsafHQ0u/HIIA7FOdQh4d2d4TtTYPoBJXKXAsrK3N51/oqegNRtjUO8setbajAH7e387+3LeSRnZ185R+HiUhg0gu88JlLM2TPOz1ByvOHM3vbG4eriiNFQQAOtLvpC8Qx6gR0Iz5H+2AESRVQgB5vZuOjlJCpFgRQ1PSFwv+90pDMUD15sDMj6Clzmuj0RRGBf712Udprly8u4dfvXUu7O8itqysz3nfIcDUYlTVvoykGPTFJxROK0ReIJM2L/dH0zyECB9o8tA2GkvK1wajEa/V9mA0ilywoSnCkY0kqSq8/SqHddMabCpoNOtbV5CEIEIxJuPTaRPrH148yFNZsa0sPShaX5dA0qClmukzpC7JIysJYUUcf4P+6o5WrlmhKbRfWlQGaUEA2gaA3jnezr93DJ67IpCd994XjWAw6PnZZer/IzSvLeHhHG9X51hnxRbMYZ2dh0OXLvF9nA+HQ6D1ekUh6Vad1MERNvjUpC68Cj//LBViyBJ7v+e0OWgaDyIqKXtT6b2OxGO/fWEt5roUrFxXz6J52tjcNYjaIxCWZ79yxkr9sb+VAu4cciwFREDjY7qXHF+G8OXnoBYH+kIxBJ5Cf4ut0rNtHICKxojIn2Qf0+y1NvHC4mxKXhdyE/HaOWUenT0t+rKsZ35D2b3vak38HR1SlPePunR3zC228cqyHi6ehFnnLynK84TiuaQb8pwqj1w7/OdDYF0RVVYw6kcFgDIdZT6abXTpKcyx85upFVOfZePOk5oN33pw85hTaeOFwNye6fMjAjsYBrlxcjEEvjipAYNCJuKxn9vw0EZwVQU+O1UBpnoXBUBxPOLMMbtQJhCSV5sEw//daE19721KuXlKKxajnY39MX5A+eaCbH71L+/upA538bXc7NQU28qx67r1kHpuO9tDtjbCuOpc1NbmUOC388OUTdHjC9PqjxCSFH73UMG7Q8+bJAbY3DdLU72d/mw+nRc/P716b1QNgwReeISar/N8bzfzxnjVctLAkWcY36XXUnEX0mH8m/OtDe3hsX1fG83ajQFW+nRKXGVlWcVn0uEMxWgdDSApE4wpWo4jIsHhBJBbnwV3taf0mEUmhw6tt0eMfoHVAo1L4IxKSoiCrAp5wH/OL7RQ5jbxe35tWDfn8Y8fTzisiqWw90UdVfnXa825/enDijY6dtexLLPZjssrRLi9raocrLi8c6kyKEbx8NLOnKZig9Skq9PujVOQNX9vHuoa55FnYLtx5XhVbTw5QYDcRjssZDcmXjEIl1V7TkiAVuZbpZaoEzYugy5cpxz0EBZUTvQF+v7WJ+25YjCAI7Gl1c7zbT1SSKXKYaXeHONrlp8hpwqQXaRsMU+Iyc9f6KuKyQlRSzjipatDolotKnayuzsGVskB89ujgqPvsaxsWpfBG0wObVvfwtfeP/V18953ZjzGveDgo9wQjmA0iiqIm+36G8ItXT/CN5+oB+PUbLez/72uSr33hsYM8vKsVAa0J+JNXDEtT24x6ynMtFDlMWZypJo8+bxA4/dX5yBmiFOgbEb/6IxKfemgvS8ucGT4VAmqGwmFUkrGQfp8GE74cgqr1Cw4VVzxRuOUnr7Pzi9fgj0q0u8P8cWsTg4m1wkX3v8y337GC/e0eHBYD3nCMbz1/DF84zvdekDjZE9REMSSVfxzoYmGpi7bBEM8e1LyGInGFCxOVu99uaSImKRzp8rO6KoeTfQGCURkBrUq69WQ/d6zNTLqk4ms3LeTpA11EZZUL5+Xz1wl/q6Pjw3/cjScU47X6frb8+2WYp+DkKYrCKfVVOYfp4YJ5haytzuNkX4B7Lqjh//0wyOhcIQ1LSp2cPyef329tot0dxmE2MBiM8uiedraeHEiKdTx3uJt/HOhiXW0ed62rOuurOWPhzJtVs8Bs0PHeC2qRFJUih4FPPnQg7fXYiBLQl548xANvNnHNkhL8sdGzh55QHFUFl8XARy+bl6CsxOkLRClxmHnjxABOi4H/uGYhe9vcfPOZY0QkhcoJeIh4wnFaBgK8Wt+PWa+pyb10tJv3XpDps5N6/j97tZGaQieVeVY+esncaXl8nMOpw5L7niaYZX1hFMBlMbGg2IGkKrxxvJ9AVEJAW+gbEtnJQFRBrxNYVeGksS+EQdAqBkOO40adkHZdRGStMlniMmMxiLhDcURRIBKXeWBrC0e7h7ProgDVeRaaBjIrLZ/7+yHu3JAe9ESU9IWjHpioJUKONb1he0fTcJOjJ0tvTWo4NaQCN3zeYysMHOrw0tDjpyfF3DAVsZhMMCaTa8+cuIscZm5cXjrt+0lWx+5XADDpBERRYCAQo8sbodRlxmUx0DIQpMcXIcdqQERgIBhFkhWsieCm3x8lGI3z4I42AlGJKxcVs3QMn5DZgNWo479vXpLx/H+/bSnX/HBL1n0GJ+hNNZYvY1GKOatRJ6ITBBRUjLr0a/eRXcNZdN+IisLzh7uS0shP7utIC3oe2NLEs4e7ybUYuXJxSVZRmcngQKeXq1eMvfg9FegbODMqPRYBwim3szsUo9sbodsb4YYVpTy2NyVZJAh87JK57Ggelh+PypmDq9WkozzXSqcnzLIKB9ubhoPpvsRg7DTr2d0ymAx4QKNHxiSF1VW59PkjfPHvB9nZ7CEyYgxRgMWlWhImlSKW6gFZYDfR7Q2TbzMiSXKaDYEajlMygevGarVy/OvXJx//9Yvj7jIuYpKMrKjEZWV088NzOOvx+w+sT/790Qko8lxRV8A3njnKwQ4P7mCMDbX5KIpMU28AOWV9EY0rdHpCBCJOev2Rc0HPbEMnCrw/Ycr5yK42lpU7ODiC1y0yvKBSVDjRG+Rk78mMY62rGs4YHmz30O8LIyHgDsR44XA3D7zZjKpCTYENUYBAt0SORc/vtjRTmmPh3RsquXrR+I7rAjH+nqgC+IDl5S6umMB+ol7isT0d3L2hakZc2Gs+9/Sor71VTcxOJb70+AH+sL0t43mrUSDXakKSFYqdZiwGHS8d7ScQkxJmllp20mzQoaoKgZimfX+gzUtZjpU71pbzt90ddHnDLCh2cNOKcn72cj2eRGVEReulKXaZCYQlGno1mZ8Vla6kxPAQ1tXk8sGNNXzkT9m9QpQRg+XI6uN4S9TUey3Pkr7oNBn1+LOVabIgJqUHRZ+8Yj5feVrL0s/Jklhoc4cJRGXiikogEqcg5f5oGwzygd/vIhCV+OglczKSCwfaPbxyrI+yHDO3ra6YMu8dMpMsI+GyGjHqRCpyLeTZjDy5v5PGviAGnciKyhzCMYV2d4i9rR6q8qzcd+MiDnf6WFzqZDAYx59YrLcMhM64oGc0bDk+uoDHsgonO5q1Kt5Y5Ig1xaNTDn+9uZF7LtYoaVazka/dspRNR7p57/npv3OpU8eQ5svIX8ms1zF0dVsM6Wfy0rFeQjGZcCzM5voebl1TNcaZjo8bV5RNa/+pojc0hinSaUR4xJc/RCctcZlZVuZMBj0CUOQwsXOE990tP3uTz145j7evH/59tXndj6zA/rb0MW8I9T1+mvqDFNoNyYr025YXsa42jzfq+/jxSydGFUEy6gRaB7WgsSzHwi2ryglEpLSm/t/ds45dzYOcV5PHLT9LD/Lz7UY8IzzEvOEY33r2GGajjs9fV4dOd2qUHJdX5LCloZ/qfBvWs0gt8hymjolQoT/18EEtiSpCVIFWd+Y4LaCJGvT4ohzs8HD7mvKMbd5KOCuCnlT8dksTR7syG1nnOaF+hNKiAhTZjYQlhWVlLv7ykQ0AvHykmw/8YXdyOx3wjeeO8Zkr59OT4ETX5lvY3+HDpNfx45caiCuakIGALm3gOtrlY1+bh+p8K55gnGXlLirzrTy0Pd0Y7slPXDihz7enyc+66hLkMfjt53D60e0Ncdm3XyELu5Jiuw5/TMWsF+kOxejxRzjc4SGmaJnHiCRjNerwhiV8I6ofcQX6/BF+u6WFEqeJi+YXMhCI8s1nj6KoJCs/oFVG9re48UbiyeeOdftRRsziB9o9fP2ZY2n7pmIwlF6peHF/G2tqh6lho+03hNS3O9IbYKNzWHHr4vkFPLZPo4UU2cYeXkbO/3FFQC+CokC+I7NJMy4rxGUFnaipzqTihcPdtA2GUFSVR/d0ZAQ9R7s0sZJ2dxhvOJ5VmWyiGO/OdAdjzC9yYDHp0AkaFxsgz2akItdKbYGNQx0eLEYd/kicUpeFeQmJbkVRWVTqxB2Ksa4mvY8vGJXY1jhAjtXAmurJUadUVZ1SleuNE30caPeysjKHjfNGb85v6R1d5vZA6/BrYxEnjw6M/s3ed2N6denh7S3sbvOQYzWwYe5wH8XWxtFFDn7xnrW853fb0Ysiv33/hrTXShNm0jpRYHHZ9APNzScGqCs7/fS2t58/h/uerT/t7zseihwmvvfOFRxs8/L0gU50aBUY0Khv84vsacmULl+Mzz52JC3oOdTQnqS0jazSANz9m21saxhApxNxmPUU2o3MKbSzsroAURCQ4vG06y/1/QD0gsptq4cl0Ie8e1LhshiTycumvvQAU1IULq1Lp9j+8MUTvFqvKQ0WO8x85JK5o3xD08PBDh+huELbYIhQTM5Q6JwIXj7Wy+v1fVyxqIiL5o9HnDqH2cZEgtuh63ssxrpO1OaHSFwmGJVoHQxTU2Cnxxfhqf2dNA0EWVrm4qYVZRzv0Swzlle4MrwqA1GJZw52IQDXLyvFNsP0bFVVeeV4L+3uMBfPL6Qmy/05EZwVQU9cUrjs268gKSqFjuxlt5EBD2iLN5MOBoISg8FhDv5H/rQ7bTsZzXtjcZmLVZU5xBWVlVU5dCTUg5wWPc39IYw6keXl6cpEr9X3EY7JPLijBbNeh9Wk5+fvXs29F83jtQZNoUU3zlojdfD92nU1rJhfRlGWRd85zA62NfZw5y93ZX2t3CEyGNHM7E72a5NgMDZMMfFGJGoLbBh1Au5Q9rbDUFwhLseIxyVaB4IExuD59I2gChlEMJpEfCmjWjSu4g/H0CWodFY93LiinKPdfkpdljTjXoB4LL0/ZTLhdokjvSIzv8SJTuhGUWHhOItHg5CeqfKGYkn6UYcn07DCE4qjqBrfPzxCGaoiz4pRLxKXFUqzqNqsrMzFHeqlIteSYRQ8WYjC2F4/gZhKQ18Qb0RTuVtR4WJni5urFhcnDYj/tquNgx0+5hTY0kQVRFHg2qXZK8JbGvo5nPDQKHKYJ9Rw7w3FeWR3G5Kictuq8kn7iOxt9SArKvvaPGMGPa/Uj97TExljwi2w6ulP+ERdsTj9+GaDSCRBgZyf4nPW2BtgW4sHgL/saOd/U+TNv//O5XzyYY3+bBkxu+lFkUBEQieAWYgBw5XCv390A7/d0sKGuflJGe7p4BfP1vOhS+ZP+ziThUE8c5NlhXYzR7vb8UfjyYBniMrb7Y1mBMSpn2QsxsIQtjYMoKggSQqf2VjDpqO9xCQZSVb49nNHeelYb3Jbk15gZWUuDrOeF49qz4ck+OazR/juOzMlqyVZ4WRfkEKHKZkwGflN9/rj3PqT13nxs1ckn8tN6XubLmVyLHQnkrWecByLYWpV7N9ubiQuqzQPBM8FPWcBQlGZ6a4Sh4zEZVXrXTvRE+DNhn7Kcsw8vqeDw50+wnEJnSBQkWvlb7vbMOo1U+D9bR52tbi5fU0FRp3II7vb0IsCZTlWjnX7yLeZON7jZ2m5a1Qz08nAHYonK7zbmwbe2kFPpzdMyK0tKCdzO6tAm1fLah/rCfDKkU6WlLkYmSSan2diQYmD7286Rm2Bg1eP9/LS0R5sRj3/evVCrlpcxId+vxMR+NueTj55hTaZ7Wwe5ESPH0EQ0IsigiAQkxTiiopOL+Iw6ZAVhVLX6IuTSFzmO3cs5xvPHccgwuttMd6+8ZxwwZkCVVWzBjx6UZNxNJpMEImgU0df2TX1Zy7gjToBSR4WMogrIMeUMTPh2eCPKuTZDNTYzXR7I0Qk7RiDYRkRsJt0FDmMtAyEMOgEjHqR3hFN+Lva3VmPPTUIKKp278Xioys4gcbzT8WO5uGFc38gC8kuWf0UMvxIVlfmUugw4Y9IXFqXOWEvLHGwsCTT8HQqMOogMg6Dz6DTVHA6PWFaBkI0D2g+YENBT1Pi9+jxR4jEJGwT8NcYUlXSi5nO2aOhqT9AfY8fWVE53u2fdNCzrNzFgXZv0t9MUVVeO97HgmI7pSkTWZHDQGu232wcLC5zsa1R6wOrzk//fWIpPV8dg4GkmIFRN/pdcvOqSm5elb2X5qafvpGkJl74va3s//KwyIHBYODeS+dl3W8sfPLBPexuHuRPHzw/7flMe9zTg9aBKbpbngYY9SJ1JXZ+98qJtOdNBh268TKD4yDPok/r4/FHJQ51eFGAjXML2HSkl3giU/He9ZUc6/VT3+PLqJKP5of34tFejnb5MOpF7tlYg9WopzbfysmB9GpPQ396T9UnrphPaY4Zm1HPdctKp/UZJ4ovPbKPr9+5ZtL7FdjNdHnDpzQ4mwgaev10eCITNoH/Z4UyqfSktnbWi+C0GJJVGElS8EbiyApEJYVAVOKJ/R10+sLYjJovj0EnkGM10uEJEZMUurwR8qwG/r63k6gk0zoQwmUxEIhK+CNxKvNslOdY+NvuduKySttgiA9dNGfan9dh1lPgMNHvj2b12pNkheAEJLXPiqBHVUnKWZr1Yy+kUmEz6gimZITbvZGMPoeFxXaKHGZeb+hPNI9rEpqyomXpX6/vZevJPup7A+hFkZquYS7xtpMDlOdYkBWVey+ew7OHulldlYPDbGBukYWYrBCTVJwjU44JeMNxfvV6Iy8d6yEYjWMx6IjGJ7vsPYdTicVfeibjuTybAUlWCMcU+nxRLEYd3tDEfrfafAtLy13Ud/tp7A8mJ12TDlAzy9AGQXMnHw0Wg4AOAX8kntHcr6D56PT6orT0h5GBSExiQXH64vL8mql7ijT0+Ziboqy1+fiwgtzhruy8+yGM5CTHYsNBULa+GZ2gBVOKouIYUa3Z3+4lKsmIAuxp9XDn+uqM/UeDqqq8eryPgWCMSxeOn+FcXpHDjmbPqK8vLLLykUvmMRCMsrIylx9sqkdWVfam9C2Uusz0+6Pk2UwYEt9D26C2gBqtgrO+No8SlxmH2TBhlSVJUen1R1EUldgUjCsvqyvishTKTrcvws9ebSDHauDHd63CmJA3Pd41+YAHIKYoSQEDhyn9ekg92+aBIJck/s5zWMm16PGEJWryJ55BTL2mfCN4qs/saOdjj+0n1yyy97+vm9DxvvnsYZ7cr/WmXPWDV9Neu27il9+M4omtB8bfaBYRjiv4RlyG+TY9H9xYw9efPjrqftkot7X5ZtrdERQVllbk8PqJ4YDlL9tbicsqep3AC0d7NHpxIlPxhx1tyWOOEIDkyzcsHuW8tX01iq12JmqWReflczIXY7evOb2CFl/LUqmaCL56yxKOdvlmtY/QG47z1IEuVFWjCZ/D6NBPkK5s0AmIqEiK5kkVlxVCMRmHWc+cEicIKp2eMM39IUBFrxPJt5lwmA3k2wp4+5pyynKs7GoeZH6xnbkqXFFXyGsn+olKMi6rHodZT0xWWFrh4iMXzcFs1GE36XGH4tMyzE3/HJqBdVSSsRrT19SSrPDgjlb6AzHWjeNpeVYEPYoK5sTCoL4v80a4eJ6N1xsyM1zBERSYX73ekFbluWZJIUc7/Wxu6NecafUiJ3sD5FoN9PhjCMC2430EE2ObpCisrdW+0EMdXvoCUVRV5cL5hcwvdqRRMDafGEBVtQuudVBT0RoIRNGJWtQMWi9HOC7jNBuYU2CnwGHkv2/OPuiew+nHDzYdY0RfKkV2PXk2E8d6NKqaTlGIhGVUtEnUKIKUKBfrBDK8pUpcFvr9YZoHQ4iCgEFQMRp1bKzN42R/kB5/hGBUy+HY9LCqJp8+f5T6nkDWvE6O1UBcBkEVkqXqVCgqaXS5I91BpGi6qlu1Pf2eqiu0cizBV7eMQxteNCKA6k+p3oynZ9DvT684HerKVJtLRU+ikqAAB9s8VOcPLzBMBpFwTOv5kSegapOKdneYzQ19ROIKpgk0hzb3j90svqE2n7evqSASlwnFZNbPyeNET4D1tcODsV4UEEVt0SUicKLHz1MHunCHYtTkW7lqcUlG8CMIAtX52Uv6iqLij0o4zfpk787JvgDbmwYpdpopcZonZGY3HuKSSqcnRCRuIiopyaDn01fN5aubMoVjQDMVHG35crJ3eNx++Xg/9166IOt2vYHha8Nq1LOhJp897e6sVb2JYOQV8rHH9gPgjijc9fPNPPgv4/dgtqSoI46sGFy4ItMj6HSg0nFm0NtG9suAVoU62ZdJ8T3QEeDOX27LeN6YsqZruv8GYrEYi768CVnVFBLftrKcH718ElUFSZHJtxkYCGqZ6bJcK76oDwSBm5aXcue6av7jb3t4rX5Qm+tVEpVwPb6IlKSrPnmwmxtWVmScy+V1xextdVOWY0lWXONZxpn83JwJfkMzi+Nfu5a2wTClLvOUFSodZgPra8f3GTqVMOgEDDqRmDR1L7V/FozV913iMFKZa6U3EMVq1BGJy4TjMhaDjrIcM6IgJqs9d66roshpZltjP4OhGJctKGJJuYveREJ3qPJ3/tx8ynIsOC0GXBYDN60o5UC7l7vPq+Zwh5cnD3TiMNmJKQpmdJTnWmgeCCVZAjMBnShkBDygrfX7A9os0zo49vw8paBHEIQLxttGVdWtUzl2NuTZNIPPaFzJ2sCYLeDJhlZ3HLsBQnGwmfXcurKSFw7vQUWrJulEgUAkTkRSWFhkIRDTVKKIDAdPj+5u55blZbx0tJdcqxGjTuCaJZkc/KvrSviu8wSDwRjXLi2hoVdb1IiCwB1rKyh1WajJt7GuJpdcm4ElZS4unFeQ4T1yDrOD5w528oOXMhdx3rCMThdPTuqyrGLUaXxwEciz6RmMKJgEFZtRT38gnrbA2t40mJxgBbS+hSK7kXZvhFVVuexpcSM4wKwXGAhJ7Gx2kztKD0pFrhkUBW8khoyAxagjMEIoIduw2ORNX4L6xfRKT6d3eNAIZymspsZWgi59CLl9TQVff0bzB1pZMfZgZx2hoOUyQxYv1KzwhdMDpjKXhTXVuYSicdbVjp3pyYb67gAxWWFu0fjUUlUZu9q8uiafJ/d1sKvFTa7VyPlz87lzXVUabaTLG0FWVNzBODFFoxWoqsqJngAxSUFWuvnwxROnBDy6p512d5glZU6uToxHrx7vIyYpWA06rl9WwsKS6U8+kbhMty+CNywhpJCNL1pUDKMEPWPla8+fk8eTB7oRgXetHV0xraF7eLEcDod5NuEB9cCWVr5807IJnbvdIBBIlE2rckan8DRmoaNmw8/uXsvar72ANxTn36+ZzyceGH7taKdnQseYaQTMecDoYg6nCzYB/CmDjy8c59E9HRkVtiG0uTMXKpYRjdqCqKcq30r7oJaR/vHLJ5NjaUNvkG++fQUXzc3l2y/W8/LRfmryrTjNRqwmPYVOE1fUldDlidA0EEZOUIvrShx0eSO0JBKTNy5Ln8tlReVAuweTXselI3zAPnRRDV9+8ljacxfPO/3iFQCP7e7g8X3tnD8nn89ctXBWzmEmYDXqeee6Svr8UeZNYCz+Z8Zohfs1lQ5uWVNFY1+QnY0DuMNxOj3axGo36ci3m4lIml2L0SCyt83DO9ZV8oEL0+ebqvzMpNtQH81gMEY0rrKw2ElDX4BjPQHsJgPt7jDN/UGWV+RwvNuPy2LgeLc/OSedKrgsBtbX5tE6GOKCuWMH7lOt9Hx9nNdV4PIpHjsDOVYj2z9/JT97pYFvPX98/B1SoBfg2kUFPHVEK30H4loG/qblpRzt9mPUC0QlbeT0R7U+CJ0o4A3L/Pzutbx0tIsfv9qUPN7BTj+XfPc1vnDDYjo8YapGybw+uLuNSFymLMfCv189nyPdAVRVi84HAjFKXRb0OpFrl54enu85TBwvHOjgX/6yL+trcUWlzG5CVcEfjhGOq8mKhgoU59iwhCVybQa6POFkcKATNLWV1OrjEFXLHYrRH4iRY9YRjsuIokBc1hr3FFVFRqUqz5KcmIfQ5YmkVJJUqnKMtMTCjGFNRbHDwLLy9Il5/bx0FRb/OKyC1MMHRpTC9rd5kn+Pu3gcIWSwuiqfl+q1/g59lmTl/CIbJ3qDmPUCVy9Lz8bOLbLjj8Tp9UeZP0kzX6NeZHmFC0lRqZhAw2VtnpWBkH/U/qtfv95ATFGxmwysqsql1x9lw5z0gXhFhUYhKc+1oBdFlpW7CMdl/FEJh0k/KZ8ESVZoT5h8tqT0GJTnmPGF4xQ5zTMmY6syJP0M/kg8qRK1v3VsKuNoGPJqUgBVTg9kzfrh3qn/vW1l8vmjHcM0wckQ9g599Xoe3taI3WLkhhXp10+ly5js/9x+3zXZds+KXfddnfz7EynPd3rG80o/NXj3hsX891Mts/LeqRgpWS0lopM8mxETkPpLW406/vihdVz53c3J57LR2QRBGxNlRRMeTx0iSp1mrlxczIGT7Ty8s4NwTEJSINccQSeUsezLz+JPcIf/46r5/N/rjQC4QxJXLCrmjeM9GHUChSOEWfa0utmcoM2ZDCJzU8aW3QkxjeTn0Au0eUY3LT6V+P3WZgJRLbD80EVzcMwQpWg2YBBFDDoR8ZxH4ZgYKbs/hFZ3lMocC33eMG2eEL4EEwUgGJVp6Q9Q4jJjNemS3/FkHRxyLAZqC2x0eMKsq87DoBN54XCMeUX2ZGC0ojKH/W0ellXkTPETTg4b5xWwcQLbTSnoUVX1sqnsN1187LJ5HGhzs+lobwZtaDRIKsmAZwiKqv34f915gkIrdKUknxS0hWi3P8Yt/7eVD20o5e615Ww63kOPX9vQG5F4Yl8737xjOVa9np3Ng9QW2NJ8Qx7Z3U4wGscXifPjlxv47DWL8IXjGHTijDVUn8OpwUdGBDwLim14Q3EGgjHMBh0xoDzHwuFQPK2ZUAX2tXkpdZkIuSX6EvQtASh1mrCY9LQNBJOFQ52guZF7EuWUrU0ebEYdJoPIRfMK2HS0BymuYhLhmqWlPLi9BX9KJWfkPXCiPzs9TEycm4omEOAOpJdTXtvfxoqa4UyMgbGz86lwjehX2940LEbQl829NQWOEbKqqQt2Kcv9fdvqcl461kuZy5JBffjd5kb2tLpRFPj600d47OPp9KSD7V7+vq+DeUU23rm2CjFllC92mrlxRRmDwRhrqtMDwGxodIfHXGwf6QpgMog4LRIXLyjk/DmZmadbVpWTZzOyrCIn2dt0wdwCVlfl0u2NUDYJtRu9TuSi+QUc6/annf/Vi0socZl5+Wgvf9vdwTVLStI8R6aCEqeJmgIbG+cWpAkZdA5OLejZdHRYUevTjx3jlvXDkr6pYhGt7gD5Ti3zuK9z6gHFOzdkr54ZDHoghiho8vQlY4jPTASLKmYn4//L5zJpYrOBkWsoh1nPnEIbNfk2fvhSupDBka9cS68/fUxSgWXlTo53+5IVyrisEopKyXtPZFj2uiLfnFXhzR1R+MkrDcmAB+Bbm07gMEC+y8rHL5uLXhQ50uXDaTZk9DumLgZHLsIPtXvSHufaTdy0Yngc9YRi+KMSlbnTu5YmAncoRK9fxqwDWxb6z9mCcEzmzztaiMaVs8ajbLYwWiKrLxDj/b/PrjarAAPBOJKqUp1vZW6hg2uWFFPqGh7LVVVN9OLoEQUBTziOy2JAJwp4QjFaBkIsK3dxy6phP58l5S7efV56I+NF8wtZUOzgH/s7aR0Mcduq8hmXsZ4Kpn0GgiCUAh8GykkZ61RV/ch0j50N379zNZ95eB/PHe6e8jGuX1rME/s1H52uEcnoEpue7pTF2ktHunnb+nlcs7ScP73Zkhxw97Z6eGBLCxW5VgaDMfa0uPnIxXOSfNpLFxTymy0BZEXlmUPd3LC8PK2q0+OLEJcVKk7DgHgOE8d5X30u7bHJINDrjyGoKooKoZhMy0BIk9PNQrUE6PJGEYQUsTG0AKU0x5Q0FQVtYtelztxonOYCu4kl5U5eONyNrEK7N8ZLh7sosBvxR8fue8kGk0FMZtRlFd44kZ4EONTdm/Z4Mo7ex7s8lOUNZz+LbLqkKWC2ITk1g1vkSl/Y9wfGzpJ2e6MM+GOgZppctvQHkhXb+p7MRfEju9s43u3nWJePSxYUZQQVi0onHgxEx2lWktEmjpoCGwa9mFUNaUtDPy8e66XbF+Gi+QXJccNs0E1JinNtTV5SGW4Ioqjx44eOHYhO36vdYtSjE9Skc/0QHtvRPuo+dhEC09Rn+ejvd7Lty5qZ8gcuXsBXnjkxzh6TQ1OiT0tR4XsvHOdbd6ya1vGWFM8cj30ymFPmAmZSjXFqGClrYdCJvG2ltkgaWcXZ3jjA1pMDGcfY0ujmgw/s4t6L53DFomJer+9DQEBI7J3aw5jaXzUSnlBmCscfB9Uf5ViXj/+4bhEb5+VjMeozxFVWVeZi0usw6cUM357GgfRALRyTiSfmhDZ3iPseP0RUknnXeVXcvOLUGj72+rVJJCJDc7+HOUXjJ2/ORMQkJSnI449MTRzlnwVxRc06x44HBc0b640TA7zZ6KahL8h/XluXpLO9fKyXA+1eipwmbEY9Tf1BqvOtXFFXxLt+tQ1vOM6F8wr55u3Lx32vY91+/BEJf0SieSDIkhnwQJsuZqKB5HHAAWwA9gHFjG/qPiW0u/38fV87O5szB8iJYkmxjdvXZDYqAphE+MG71qY9d/fGebxzbSXdnlBadjciKWxr7GcgsVATBI3z+OOXTnDRN19m05EeylwmCuxGREFI65P46842vv7UER7Z1c6RztFN/c7h9GJLfQ89wfR+jcpcK6qqKZpolRlN0GJcuciUvp08m4Ecq4E36gfT9rIbdSytyE3zcfKEJToSg0M4pdzR4g6zpjqPihwzC4tsyX1KHAbGs2VQVZXafAuiADkWfUawMacwfbE8mfVpVE5/876U7y9b50vq5+/wpC9UDPqxh3BPOI4z0d/kDqcvZFINV7N9Hfk2I4IAdrMeyzQbZC0m47jZIrvFSJHdRIEte+/I3/d10uuL8MqxPrwj1TJmEAuLHNjNOox6kcWTCOxGQ32Pn33tfj758AGaUgJ49xilwbECno9fNNzHs/Pf1o+63fzC4eTQ3qbeUbebKooT/m8CcM/G2rE3ngCeOZjpfH46sGrOqTG/nC66fREu+ubL3PbTLSwsTB9/evyjJztUVWVPi4dXjvVyvMePzazDbBDQ64S0anDHiKBnSAlQAD5ycU3WY8ckhUf3ar+Ty2rM6nAfCsf5+tNH+d6mYVp9tzd7pTEQlXh4lxb8N/YGiMRlVBWOZTFTP5Vwzw6zckbgshq4enEJy8pdXD7C6PUcNAzZNUyn/1tEu16jkkyXN8yBdjcxSUZRVOq7/cQlhU53mJaBIJKs0DYYpm0wlJyr6scwo07F/CIbvkiMHl8Ew2Q5dKcIM1FryldV9d8FQXAALwE/B3aPsw+CIBQBvwEKgNdUVf3cWNuv+9om+gIxTDoQhMn92AJw28pSevwxmgaCfGdTdsdqSYUP/2Fn2nPbmjzoDUaKnRZ0goCiquhEyLcZCMcUSlxmFhQ7+Oxf9/O/z2hNjXkWHW1hGQEotBu5aXkZNy7XMj0vHunmD2824w7FiMsK54/TdHUOpw/v+W1mSbitP0hUgWK7nvW1eexp9eAwGZDksZvZh+bj8hwz5TkW8m0GTvQE0ibquKLSNhjGpNcqMUneraRyz292pB1vcZGd5ZUu6kqd2Iw68q1Gfre1iW1N7mFRATSz0pHephFJZWm5C5NBu91r8tIXHWZD+mM9E6/2FDnTpZMnc2eWONL3fce6Cn6a6J/Lt2YOTSa9QENvgBKnmSJ7ejBx28pKnjnUi6yorM5CUbtofgGbG/qYW2ifsNzzaJBkdczvx24UqMm3MrfInqQARCWZuKwm/XUWFNs52OGlyGHGkXhuyHB1ZAP3dHC8108gwac80uVLU5CbLg53DFJbpGW/P33NXL7ybHYhg7Hw7zcs4xNXLEKvB71+9OnILw1fWTNhHjoS275wNd977ijXLS9iUVnOtI+nH0NZ6VTiQMNsOQSNjaFs72AwhlFMHzs3zs1HGUVxschhxmEWee5QF8GYhNNsYH2NJvjSm+IL5RtpnKUo3L6mgu/csQKAn73anOXoAutrM8eKw51eWgZCrKnO5cMP7OJQp0bd/LeH9/H6CU3avthp4oPnV/CbN4crnHaznttWlQFw/twCtjcNMhCM8fZRkqynCguLp2baeKZgcZlz2jTctyJkReVvu9vo8ka4eEEh4ZjMVKUehtYIwZhMfY+fLzx+iId3trG8Moc3TvTjCcVYWZHDolInBzu8zCmwMbfIxqULizjW5eP9E0wMdXmjuINxurwRHtrZxscvm0e+fXZ9oGYi6OkUBGEOsAX4HPAcWrVnPFiA96uqOiAIwsuCIBSrqtqTbcOm/iCOhBxdVIYc0/jmgHphuC9ABR7d14WApl6RYzXypw+tY9vJAX7ySmNyn0WFJg71pmedrAZNqWJFVS6rq3PY1+ZhIBDlqYM99PjjfP7xQ/zX1QtpTpHJcyd6NFSgNxDjeIpfSTAmYzXqkBQDpTmWcwZcZwhu/enrWSscQ1TwnoDE+jlGagtsBKIS7lBmdtJuylRP6/VH8EckLpybl9GnYhRVBoMRIpImwRpNadIZKUbQ44+yq8lNICZxpMuHLxQnNMLTSRAyA54hdLhDxCSZUpcFmym9yXXAm145nQwJqqM/yOqaYdng0hwT3YnFiDmbGkEKXjvWxdLK4aD/zYbh83CHMs/i1WO9ROMy7e4QR7q8rKgcXsCbUpo69VkySl94/BDt7hD13QFuWFbCkvKctNcbegO4QzGWV4y/oI5JYwe8oZjK+po8PnBhLUa9iD8S5y/bWwnHZa5aXMySMhdfv2UZLx3rYX1tHjqdSIcnzGO7tQXU29dUTKqnZyw4TIYk1XJkD9VUoEtQ5fQiXLpomK77wykEPAAPbG3k+5sa0OsEfvmeNayuzh6UeYLDVaU906j03/Lj17CbDPzpI+kCpEu+/AzBqMqPXm3kuU+cT1359ILDwcDseIzU1Zy5SbRgTEr8P/35fLuJW1aV8+mH92Xs84kr5rG/zUu7O0JNvh2XJYbNZEjrbYTMqvKFNQ7Oq81la0MfF8xLlzVfU+FEEKHbG+E9G2pp6g/y8M5Wip1mbllVxqYjPagqeEJx1JTgVUFlMKSNbX3+GA/tSqd0fviiOVQXaD1BRr3If1w7O7LlESmGneklds7hzIMvRYWtvts/ad81HVBTYKPTGyaSSLLqBAFFURFEaBkMYUqwLXSigMmgozcQTVLSvGGZr986MaXMIaRaSMRlNSloMpuYiaDnLrT1/Z+BJcDdwAfG20lV1VSJGYURlDhBED4CfATA4CrCmXgTq1HEM9LBMQuyNUKrAAJcs6SEJSUuvv/8wbTX337eXDpeOoE7MbCtq3SxrKqAQFSirsTB0nIXt66u5MMPpFeDvvFCuqLc0hIbB7uHJ+lXTgxwz2+28bsPbuCm5aXsaBogLqt8/rpF6CfgC3IOpxaqqrK3bXwKwvOHugEBnQ5GWEChF7XKRcOInpuYDIoq8VJ9ptO3Nzp8kUZlNau3xRB6g3FeONKDThTQoWYEPABjjSd72nzYDAJrq/MyjD2no5ITHvFFpJawx7uy94ww+DzQMVwyz/Y9+CIykqpVWoQRn/Wl432gqugEaOjP5HeoaD1ZgkDiP8Po9Uf4R6LHzzcBqlm+zUggNrq2tgLcfV51UkGpPxAjlPie2gbDLClz8eT+TrY1DdDUH+TTVy6g0xNOTghd3nBG0KOqqmbAGpdZU52LfoLUhqp8K3euqyIuK6Oank4GcwptrFtQwK2rypPKbQBTkzGA5w71ICsKsgL/2N85atBzwfzhPNqQz9lksei+Z5KU0fVf38SOL1yVfC2Yci9+7rHD/P0TF03pPYbQPDC2V8SpwkBw8j1/pwM6QWM9+CMS8SxV8ugoiYSlZS6OdPpYXZ1LqctCgUPPg9tas5p4m4Ghu3LTST8vNBzEbNRR4jSn9REZjTrebNT6nt79620UO8y0J6i2LotmquiPSORaDTz4wfO454+7yLEY+P47V3Gw3UvzQIgFJXaOdKbPGd95/jjra/NYM8o1fLqg053zt3krIsdqYFGpg3Z3mNXVuZOmi8nAyRRFVX2C7p5nM+KLSKyuzOGShUU8f6QbX0h7/vK6Ql4+1ktVng1fOM5AMEpVnpVwTKZoFN+3qCQzEIhR4jSztjqXgUCMbm+YRWXOGWUxTBXTDnpUVe1MeTgmRS0bBEH4BFCvqupg6vOqqv4S+CVAQc0i1WXRs7jUydbGwWyHmTDetqKUXS2D/G5LE55Q+gLnno213LOxlpb+IBW5ZnQ6HXFZa6yzmfRJdRgjmlzgUHN4alUJ4LLFpXT6WhhIOf6xbj8dnhBf+8cRXjrWiyAIOM0G7rvxnBnpbGPt/zwzoe1iiqa2li22kJTMHpUhyArII90Ls2C8LWRFwaTXT9p8cwjBuMrmk/30+NIX7C3+qS+UBoLpFa+BlApNNvO+VFiM6YO23QhjKb7qdQJIGo0vGE9fJN22qow/bW9BklXOz2Kwd8WiIh7a0UaZy8KcEQ3JoiAkqyHiBCaSbNz/kXhwZyu3rCpnTqGdqjwri0qd+MJx1tZodJrX6/toGQzS7Qlz7yVzWVLmpN0dQkBgcRb6VkNvgFeOab0sKmRIYI+FEtf0TUmH0NQfJFDfz5aGfq5cWIx1EtLa2fDuDVV85R9HMRtE7t5QM+p20fjwtbSkcuzPvr/NTac3wlV1xWlJpdQeuf4Ruuypi+IPnD8xOlIoJvHfTx6myxvh/Rekn7s0S/T1uqIzUxjHaTFQW6CRcbaMEC041OHlj282pz1nFOHTV82nLxAlLqvoRYFLFhZy4f0vEcmW0WQ44AGSJuRxSaHXF0kbsw+1Dws9SLKaNm7/bnMzD//LBfT7Y5TnWtCJAo/8ywWEYhJP7e/EZBQx6ATysgTesgrfePoIf/vY+Ma2pxKdnhC59pmpFJ/DmQNBENLEsMabX8eDlJjv5hQ5CEUlqgvsLKtw0eOPsrWhn+PdfnSigNmgp77XT2NfAG8kjiiIlLrMXLygICPAVxSVh3a0MRiMkWczUuQ0Ud/jZyAYo8cXZU+Lm3edV03eNCnm08FUzUl/q6rqBxJ/nyCLB6KqqtmttdOP8w00hdz/N9Z2NQU2dn1Z804YKUupF4cHuPFgEmDziQHMeh2hSDzjpC/+5ousrsonGA2z6Zibj1xYzedvXIpBJ3Lh/z6b3C4GNH/1Ot73m210uEP84K7V3PjjLcnXH9vTwrJKJ9tPDBBWtAn1zS9cxUf+sIvtjVqVx6DTuMPnMPsYmKAhJmgX+mhV5VF890aVPMjmRTHW+8YVUCUJl91EKBadlOBAEorG4U3F+rKpm8AtGqHGsrTcwcmEdLZJN/bKzzSCZuc0jR30DFEHVTTp5FRYTAbesbaSQDjOhQsKM/bt8UWZV6RRT7p9UWoLhoe+AruJt6+uYDAYmxCXPBAZn7oUjUt0eyMUOkw8vKMNWVF5x/pKcq1GmvqDtA6G6PFGiFiN6BEwGnXcukpbbMdSBjRfJM4rx3oJRWVUVUUQBEyzWB0eqkbFFNjb2s/Guun5jN24vJwNtblYdYYxA6i+0PDC9F3/N3qSoqHHz/3PHkdRVVr6Q/zLpdkb+0fed8VG6E78rDevnRhf/Xi3nxNdXsJxNRmQDuHxT14yoWPMNH71+syq2s0U8mwmPnvNQspzLKz/35fSXrvxx5sztv/ZXau4clkZrxzvJRyT0IkCJ3sCowY8o6HAbiTPbuRQx/CY9/07VnDvg/uRFbhyUSGbjg73Qf3LRXOwGvVU5acvjX74Yj372rwcThwnm9ocQMGUa54zh+rccwHPPwOM0zSyN+oEihwmzHqRYGINFJcV4pJCVFIwG3R4QjEKHWZUBU4MBPCFtXVzqctEry9zso4rCu5QDHcwxv42D06LIUH7lxAwYjHqcIdiZ1/QA3wx5e8rp3IAQRAuBgyqqn52iucAwA3LSnhi/8Tkq6MqNKcYPOpI5wK3uqO0uocLV7/c3MKVdWWsn5dHrtVEu29438d2t3O820d1rpWlI/oD2j1x+oNu7rlwDuvn5HFZXTFdnhD72z3oRBWzQaTYaeartyyZykc+hxnESwc7x99oBKapvgtoxourKnN4s8kzqf18EvgSkcFYdLjR0BuIs6YqvXn3W1sDfOzmSR5o6HxGcOuf2D+8+AvEx16gjOz1bp2EkOHPXznJN98xLCsck2S2Nw4QkRTmFmcGcdcuKeG3W5pZVOqgKgvNqzLPOmH6V5d//K6n5w534wlL/OSVE+xt9SKgcqjTw39cu4i/7+3AG4oiKxp3WkJNMvAf39PBtqZ+DDqR1VW52Ix6Gvs0SsKa6lxKcyzMP0Ocyv0pEtj/75JyfvLa5BXLvvP8MX71RhM6UeCn717FZQuzt4N29w1fHPOs8OYY56QkLqyxJLpHXpndKXHs3M89zcn7bxj33FVFYX+nHyVhOp2K877xGs0TOMZMY+eWttP+nhOBKMCqEePOWPjQn/cyp+AY37p9Je3uMGaDjor8yS3mLXrwhuJ85ealfOLBPclK39eeq08mrt44ni788PGH9/OzN5qSzu7v2VDD2ppcgolxbihRZdSJHPzylSz44gtp19JzrZM6xVOCDV99hUNTuPZ2NQ+yuaGfyxcWsbwyZ+ZP7BxmFOG4NCkhA70IH7mwBofFSPtgCKtJR7cvSttgiOPdPg53ethxsp/6vgB6QaSm0MoNy0vRiyJFTjMP7WihvidAtzfM/nYPRQ4z4ZiMxaijZSDIgXYvdSUOrlxUzKvHexN9QSI2k555hXbiioLLYqQ2f3aFNqYUKqqqmjq7PauqasvIfxM4zAXAtYIgvJr4d/loG8ZlhSu+8yoL73s247VtjVPzJBAFcE6AmfHDTYcB+Menh09vbZmef3tkP93+ONtbvVz4jRe5edEw5UIUNFnBxWVOLqvTJvHvPF9PMCoRleF9F9Tw6r9fxryicwols40P/nlv2uPJsFL0IhQ7p6ZEEpE0ZcCpQgSspinwYxUwzGClwD6iWjOZ769tGn0PlyxMpzj5IhI6ndbTE8nC949KKmUuM0a9OGr/wEzCF5Y40KHx/2VFQVJUBgKxJDVREEVEMV10QVVVntzfwf42L9tODtAyEEqeq1EvsqwihwXFjqTvzmzjtfrhBeNz+6cm0fzC4R4kWSEqyfx99+jHqPcM//3V94++oFtVlcu7zqvisoVFfOii9IrNZy4ffvzTu1aMeoyJJhJeSSyYRQF6x/GYOl3oOfWX9rQx0Rxvc38Yf1RifrGDyjwrj2w7Pv5OCeQatMp7MK7woT/uRi9qlgOiAM0p8taRLD/2iR4/kZjEq8d72dk8yPamQT5+2Tw21OZz3/V13LGmgn/8vwsJxlW+cMOiCZ/T6cLNU8yJ/PjlE7x5coAfvXxmVgvPIR0TYMynwWE2oAgit6+tYklFLg39IXY2uznQ7iEQUxgMSbzZ7GYwGKc3EKXTE+GNEwOcNyef2gIbt66qwKATUFStN3XryQF2t2jr7+cPd9PQG+C5Q90sKXPysUvnceOKUi5bWMTHLp3HdctKuXlFOZcsKJwQhfxUYiZWP/8QBOFOQRAmtfpTVfV+VVWXqqp6aeLfy6Nt2+WN0NgfJJqFx2Yez6QkgbWJzIWIJn1bYDfyu7syqQ8j46BWT4QuT5h//es+cix65hRYWTe/Ki270+6N8lz9IEV2Ix+9dA5Os54lZU5uSpixxWWFQx0e4nEZgyhSV+rgHGYfX3pkb8ZzAhNfuKvq9AzUJkLUKHYYGKnePCSFbjXqmGz8ct3y4gx9/89fOXVJ1bkF6f0iFa6J93jcvCqdGuUaZzVkS7nXK/LTZ/aTPT6OdAZp7A/xyM7MdOvPXjnBk/s7+cObLXR7JsFnzAKdkN14NRWlLgtOs55bV5azsNhBXYmDf7+2jpoCG1cvKaYsx4LVqMdi1GFO+RGLnGYcZj0GnRacbZxXwPsvqOH9F9TMKiUgGxymYbHNHNvUKDWXLCxArxMx6kSuWzY6Ve6964e54639w1WfbPfq21aW8y+Xzs0QPPjU1Ytpvv8Gmu+/gRtWjH7NP/mJjRM693svnEOh3YRJL3LXuqq012bLGvILb6ucpXeeOCaqa6fTwdrqXOYV2VlR6eJd5088wFBHXBj+mIzFoMvq0XX0K9cm/z7ypauJySpxhaTZscWg4887Wnl0bztPHezmyzcuYn6Jg6b+IP6IxEXzhxMwr352dmiNqfjsJ0fNH48Jq1Gf9v9zOLOgKCqP7WnnRy+d4GC7lwLHxOcDgwBXLS7GpNchKyr5diMukwGDXkxbDwx5EYoimPQiFSlUyap8K4tKXdjNBow6EYtBl5yT8hN+dLk2I4IgIIoCa6rzOG9OProzxJ9nCDNxdf8riTWAIAgSiQqwqqozNkOro3geWPTQMpi5gDEBqXm35vtv4NafbqE8R1ug3b2ujHsvq+OXLx3J2HfkEtYbVajv9XG000soJhOXtQj4/evL+f0OLTM5tBbzhOO87/xq/vPaRexqHuThna1cv6yUD/5+J8cTRn6L8i1cu2R6PPhzmBn8YXcmtU1BC4xdFh2hmJyh0gbDPjayCuGYklx4pV6lk+nXGQs9/uErUkicmwp4IzEEQZhwPxtoN6lVL6KMSBEdb2sHRs98j4UXj/fyweLhJV42VbnR0NqfXunJtZrwxkbPmAdT6HLeEbq330lRUOz2Zy6regMR4rKKosi0e0LMmQZFrCzHTJ87ktV8dQhvW1XGtUtLqc638YELa4klONKv1/dRlae5Wzf0BSiwmZAUrXFbEAQ+eslcNh3podUdRC+KBKNy0in7TMOzR1Q+n6BFXrawkF0dk+f2vH1NJVajAYNOYE3N6KpXf9gxyFdu0/62m4anlpkSQL1peQm7mt3YTHoM4sQyCXabkTf/6wpe3NPF1WvL+HLKa9WZbWWnBSc9syOVPRmMpJUbRK1fMWM7QcBhNnBetQubAWw2M069RvHNhl+/ZzX/9dgBPnbZPLbWd7MptTyYwNxCO8e6vWnj+qce3IMoaFTT23/xStr2N60oZW6hnW89dwxVVen1RzjW7WdtbT5lOWb2trrZ3qj19wjAswc6+ejl47Yzn1I8evgwHz5/7fgbjsCXblrMnhb3jHp5ncPMwRuO05JgRxzu9CLJ4yffhhBX4Yk97ViMIg/vbGVVpQuHycCXb1hMgcPEa8f70OugLMdCfbcfnQhLy3O4JkU4wWrU8+GL53DdshIUWSXfYaI4oeB288oyur0RiqbIfDmdmAn1tunJ90zsPbJObqM2juug2mXBE45RaDex5EvPYTfCQEijmXxzUyPf3NRI1QTWPaIoUOo0IwiaC3SO1cBNK0q5cnEJ/33bSgA+8eBuXj3ez4IiOyUuK20DIb77Qj2KqrKjuY8dzcMUPKNOh3majvDncGqhAJ7w6Eva1MtutEXXqVCjVxleLGg+VZN7Fxl4eE8Xen36Lds6NYaodk4jEhK6cUxbU9Hdn9702zqWisEI7G4d5KKFJcPvOw4padCvNWBKKkkz0KmizT1+pWjTkR4Mosjtayv49eYmYpJMJK5QYDexr81DIBLjzZMDrK7OTVODqymwsbjMyWAohk4Ukt4mpxLesCaWYDXquGJR8YQzcxurhx3Tv/3y1JoZ6kqcFNpNGPViUuJ7PESiU6OSbT7Rxyf+shedTuDBD21gfslwxf3KxSX0B+JU5FqomWCQ+cMXjvD9lzUzXf6WXjX2xWZnjM/JdwBZ7e7OGIwcIUbLk8RklZt/9DoHEtLQ66tzyHOa8WVJdAJcuaSUK5eU8r3nD7OzVdtHAEwGgWKHmcvqismzGVEViYNdw9K9m0/2o6igyConetLTnkPiJ29bWcYf32yhKt9KXbGTLz1xiF3Ng5zo8icTpSoTGxtONdq7p8Zx3Nfi4Yl9ndiMei6tmznFx3OYGbgsBuYW2WkbDLG8IgenRc9kRsKoAtGIApEozx7uRQD2tnv5+GVzWVuTyz8OdPHS0V56fFFisszmk4MsLsvBbBB5s3GAilwra6pzqSvJbMsw6MQZsUQ4HZh20CMIQhHwX0CZqqrvFAShDjCqqnpg2meXgD86OefZmAzhSBhvGLxhLTIOxsigAnVk2nmkwW6EcFTi15sbOW9OPgV2E2U5Fi5ZqE32u1sGOdDu5f0XzOHHd63hRy+e4JJvv0Kxw6Rl/wWBv+9JF1n4zRzjtQABAABJREFU+q1LJ/FJzuFUYdUXnx5/o7comvrSL/y23lE2nACONHfBJcMmfL2TGIVfPBFMezwZirJrROBS6LDQEwyOsnX6QusPWxtZdYq9NI52+ej1x3jpWC+RuEx/IIrDbOD8ufnk2ow8daAbbzjOG/X9uINRchP0gFBM4lCHlx5fhPNq86grOfVU2D0tbpoS/g21BTbmF0/sPR/a38P9d03//Sfr0B2Ij76oGwhE+dADu/CE43zy8nncunqYxvaVfxzGm6Cj3vfEQR6+d9ig9G0ry7lpeSniBKs8AD97tWnU1/KMs9Nc8/zOhll531MBnQCHu4ZV13a0eMbd5+l9rfzolebk40sXFvL9dyxn5Vdf4vdbtecNI35iOUWO85ZVpTyytyvjuO85v4b3nF8DaBLbj+xqS1pWpGJb0zQG0xnCv169fEr73ffEIWKSzL42Dzu+MCV9qnM4hRBFgZtXlCUfd3oiTMeKWAXcwRgNPQH2tXrY2+LGF4nTF4hi0Im0DYQYCEY50Rugwx2msS/IvEI7rmnaFMw2ZqKn58/AMWBISmkA+N0MHHda6M3SIz2SCmQe59MHY5pp5F93dYKqsqoql3esq0xyILc2DOAJxXnzpGY8+dKxHuKSTIcnTCzkZXvTIHJKIvzKhUXMzxIln8Pph3vqrThnPf7nlnRvqIlpH2ZHt2/qX+RkBYIs+uEKxKqq9OH++hXlyb+LbGPncuzmU98bo6qaQl9/IEooKjEQjDEYjBGISLxjbSWSojXvxyQZY8pCu9MT5mCHF28oTigqnxbRghKXGUHQxBIKJhmATBc1n3s6+e/yb26a0D7WMSpCrxzvpdcfISbJPHUgffG6ojIXQRAQBIHzs/gcTSbgAfjpHauTf4/8lXb1MSt4vnl23neiONDumfC2ZoPImuqcCW9f87mn+fhD6Ybju5oH+bdH0vOvI2MVW0KMRQAcVgPvWFvB8goXX7k5u4feq8d6sgY8AL7wTGh7Tg/+bOoME8DQUHOG6KScwziIjeadMQEYBJhbYOP9F9Sg04m4rAaWlDtxWAwU2M04zQbqypwsKdUq8QAOsx6zcfbsEmYKM9HTU6Gq6i8EQfg0gKqqfYIgzGga1ToDLq6lDiNdKVx/vSiQ5zISdI+emk4l7rxxrIeBsMQ/9nfy9VuWcP68QuYW2Tne7WduoZ3+QIRAVGIgGGdugYW93ekXZFWuhV/fs27an+Mcpo+RXk+zgZnq+ZkKCkcoI1xSMsqGE8CdFyyc8r7FrsklAG5dVcGzh7spdphYMCJ50OMdVmTKth5ZVmLhYHcYAfjSzae+2mrSi0gK3LG2nG0nB2j1hHGHYhzs8NDri5BjMeINxbGbDZqkVALPHermQLuHUExGFOHKxcVZaQP9gShvnOijyGFm47yCaZ3rolInZS4LBr0wqSbmv90+LC9tA0avs00Mje7R+1FSz8ppGD1ovWxhEX96sxVPOMZ1y9Iv7G/fsYIrFxViNui4ZIQ09s0/fp0DCQ+WiUpNlxZa0YsCsqqyosLF5MXvZx4fWw0/2zPbZ5EdcVnh5WMTq4QIwPxi57Sp4P6ozOvHxo5A37Ohigd3tlPoMPFf19ZhMBiQJCXN2DYV62rz0ItC0rMqFa98+vxpne9UsazcweEOPyUuExVTlAQuths53htgYRbJ/3N460AvgMtqoLbAilEvkGc2sqDYwYF2LxW5Vq5fWkJYUlhY7MBo0HHpwkLqSh3kWIyY9Gd/a8ZMhG3HBUG4BlAFQbALgvAfwIyaBYSydZNPEl0jmpslRWUwOPGmz4bBCO6wRGN/kE89tA+A65aW8C+XzOW6ZaXsa/VQYDdR6jRxojdz+n/+M7Ov6nIOZwbqisw4zbM3eFzx/XQzwNemUerxRqbec9KQ4r0yEdQU2MizGqgpsCGOUKBLFSYocmby0ev7teSGCrx0dDq1rYlBUlRcFgPd3gieUAxUFREVSYGWgSBRSUavE1BVFSWxeFJVlVBUxm7SYzHosJsM+BPfr6KohFL6e7Y09NPcH2JH0yDd3un3EbishkmrNt2/bZh6NN2AZzykXmX7Do7OnDYALQMBujzhrN/LNUvLMgIeIBnwAFzz3VGFRNPw8M5WJEVFVTWj0lQUzlJC9EwNeEBLNBY6JlZJVIGqPCvbRjEBnQziwOb/vBiAZVkEJp473INOGFaeAkYNeADOm1PA7+9Zxw1LChnZ/nbnr3dP+3yngmuXlFKZZ+HShVNX0GjoDyEKAoe6/ONvfA5nLSQV+oNxXjzWx/c21fOzVxr4y/ZWen0RBEHAF5VZVZWLNUEhFwSBUpcFywwUH84EzMTQ/AHgXUAB0AFcBLx3Bo57yhGMZWZqXvvocBZ4Y7WLihwzS4vTMyf6xIJLEITkhbC2Jo8ih4nmwXBapnl1pZPm+294y1wwZzvOhCpP02CMYGz2aBDKpNx0xsaCwqk3L9YVT07m+Bevn6SxP8SLR3tp7E3vS9o4txCXRY9JL3L14szSVarcfXNv5qTe749S3+0bVSlystC8ulT6/FEGgjEicYWYrKLKCi8c6SEYlZAVbSwxG4bHk9vWVHDXuiruXFfJ7WsqqCtxICsqD+1s4xevNbI1QaVVVJXdLYOc7AtgHYNycLTLx6YjPbgnkeCZKC6dP70K01Txrc2jVwvue/Iw7rBEVFb5xeuNUzp+TJ5YIN/rHb4GR9KdXLPkSnAm624JgsA71k5cUvvv+/4/e+8dHtlZ3v1/zjnTu3ovu9rem3fXu+4VYwym914DhMCPkBfCm4SUl5BKSaEmQAjNYIqNjXGva2/vfdW7NJre55TfH2ckzUgjaTSSVru2Ptfly6M9Zc7MnPM8z92+dx8l8yTTXl/ipOMrd/PgZ+/mc7fnqqt1DEcYCCU50DbCjw90cao3yI/3dzIUmtqZcN3KCs4MRpkY7Onyx/MfsMB89/l2+gIJfnOsn0C0OCeIx2ZA1TTK5/Cdp2SVzpGFdoEsAXrN21xJyBreSJJALInZKGE2igULuVytzIfR81PgT4B6oBW4FnjLPJx3UWhqaqLjK3fzT2/aQEcwyepKx5jc9Ch/ftvKsdexlEwkIeOxmfR6nyzXz1fesJ5ffeL6y3btS1wdJGU1b2rE5eKf3pBb6Dq5W1Xh9IwUP8nHZ1kO5I9mFNhUjR5/btHeI2f6iSRk0orK709NLkR2mceHuu3NuYv1gWCCz/3yOH/x29P8NE+Pn2KIJBXaR6LcurqM/mACRdM9bKGkzMXBCIIgUGo3Uue25tQarqh08O49zXzilpXsXVGOKAqEE2kGMul7bcP6WCQgsKbaRbXLws8P9vDj/R0c6vDlRINCiTR/ODXAY2cG+O5zbchzyAHPxyfv3Djnc7x5y3htTaFpZX90+9Tv++pN1WPe9+Yi03wkqbBC3acu+qbc1hqcctOCsrlx5n0Wk4k9wmbifz6wk9XVDlwzFeBOQ40rdxGfzpLsN4qAIKChq8X1B+L8wyPneOBYH1997MK05233Ti4cvu+P9uTZc+GJp2TSqkYyrWAssiinxmUBDeo8xfXckmWVP/vlCf7slyf46mOFN5JdojA0TePR0wP89/PtXBqKME/+ORKyRigh84G9zdy2tgpvJMmp3iDD4Suj4fJ8Mx9GzypN0wLo0Z3DQBXw/nk47xiXo65utGndp398kGWff4jP/fIUvYEET1zw5ogRAHzxIb2/z5n+IB/90WH+6MeHOdETYGOdmxtWllBuN/LR65bxtp3Nl+HKlyiUKyHKcyXQH86drFvncK4XWotXK4omZ2cwZdeZ2y25qVhOi3GsANcyUZ4JPa2pscTChloX9RM8WT3+2FgK7ahRMVf0ztUawVh6rHZL03SFugqnGY/VCAKUOgw5ktWqqhGMp8ciTl0jMf73pU680RQOs8TuTAH+2hoXLqv+mQOxFL8/OcgvDnXzwLHxyhKTJBJM6L0dunwxjnUH5uWzjTIfz5PK+IL0qTNTV8U4syaB3x04OeV+r9pQy6/+6Fr+6U0bePCPryvqml67uXLmnYCnPrF17LV5QiB/sVwaw/Njs18xrK5x84dP38ju5RXMZMK+YYuetigJYDWKCIBRFHj7znFLsMMb4XRvYOxvWYWGUttYDzS31TT27E3nIlj3F7+f9G9Wg8DzF70Ffa75ZkWFHY/FQK3HjNFQXKn2sZ4Qsgb7s1pszIZISqY/45y5ODSDNO4SsyYQS3O6L0QwnuZQh29Waqczcaw7yN88eJp/f/IiX/n9OX6yv4v7DnWTmEYp82plPoQMLgmC8J/Aq4Eb0cf7ea2Em+8JpOMrd7P88w+N3TQ/eJU+nK754kMk8vzGVS4T/aHx9JDXbtIbNh3tCpDKuGm/81Qrj54bRBQEvnTPOt62q2mer3qJJeaHHt/8pWCsqXYXfWxkto6krIHAOCG2v6W+BI/NSCKtcU2eJpclFomBUBINqHHnejK3NpZw3cpy+gMJ3rqjgb+d5WXlIyHrNTx/OOdFEhhLg4kmZFZWOtjXOkJSVhkIDpNMytisenTht8d76fDGWF3t5NUba7g0HCataFQ4zNywqpIVmdql1dVOVlU56A3Euf9wD4KgG37Zij4Wo8Trt9YhKxoem3He+4PNh8fs18fGo3J/ct8JTnypNu9+2Y6nmczSLY2lbGmcfA/s+NJDeDOZP3ubLfz4Y7eObbvvIzt4/w+OsLnew6duW1/QtdfW1tLxlfHrNf/3pws6biG50n2zxRrKJTYjFgtMl7n1r2/bwZfvlfn9mQH+58VOjnYHSKsaL7X5+FRmn4/97xHOZdVfacBQMIqWeb2j2sCeFas51h3kVRsmp8k+d3GYP/35sbxNmOOyRjI6uzrF+eJf37qVXx7u4ZY1lZgWKZXeYzPx6o01HOsOcM/m/M/xEsXjtBiodlsYCCZYOc9iE4IAR7sDJGVVz0RRVNq9EapcZl6zqXbS3KGqGuKEgrZs8Y98268U5sPoeRvwbuDHmqZ1CoKwDPjiPJx3QXnys3u56+v7iMsa73skCo/kH4xNIhgkie+9ezt/97tTrKqy81ev1dMrbltbxbHuALGkwqNn+jNdnjW+9UzbktFzhXG0dZE0ZK8wDCKTFFjmUgegCsVLVpumKRbOh5y18O32Rti5bDxNzWqSWFHuJJJK01I5eUL47xc7kVW9eeBPXurgHbubx7ZJosAf37Jy0jFzQUCfSM72hzAaRNKZGi4VjeXljrEaI1WDTn+UtVYPmqbRNRKn1x/n0lCEW1ZXsr7WTedIDLvJQNOECJUgCNSX2PjojS3cE4jTG4izriZX1W5TvQe31Uha0cYMpvmircB0tOnIXjqG8nmcMmTHJm9f0cyzbR2zfi9v1oL5hY7c1fOH/+co0ZTKvjYfZ7qHWddQfEH4YnJxsS9gGn55YOq+RtOx8s8fwmwQeM+1y/jPZ6Y+x/v++wBHuvw0ldkY8I0bH/va9DTErz16LsfgGSXLn8lbf3iGtq/czeaGkrF/0zRtTODgO8+2kVKnvk//+Zk+PnnX1im3LxRf+f059rf7eOGSl4f/5IaizvGhvY388kgv7830IyqG9+5p5r1FH73EdBgkkbdd00BKUedVRc0kwtuuaWAkmuRETwhF1RiJJLGYDDxzXlcIvWHV+Hj4+JlBTvYG2dLg4eY1laiqxreeaeW5i8OsrXFR57ESTyvcvamWZeXFpRgvJHN21mmaNqJp2tc0TXsh83e7pmn/M/dLW1hu/pcXiMvTx5BcIqRU6A/EefTsAB3+JI+e87Hi/+qh7SqXhf/76nWkFZWsNGH2rpxLy6glFoLXf/fAYl/CFYHHYuAtO3OLiaeuTJiZYLR4o0eZqhX7FGT7jWrduQpt0WSas4Mh2oajnO6dXFCR3e+mzD65ZuNYl5/fHe8jNU/hfA3doHFbJOJZohVrqlxsqMutct/f7uM/nrrEsxe9hBJJHj0zwMneAN99ro0ql4X3713GW65pyBupiaVkQok0yyscXL+yApvJwPHuQE4+dlOZnZaK+Zl8Nta5x1KBF4ujJzvm/ZzBLIPry3+Ye4PPxuLKIubMqiu0jcZgKMEXf31m1sc1f/4h0ipEUtq0Bg/Ayd4gmqZxpjfE8IRg9srPP8TXnpw5kXd9Va5joS8Q55vPtPK959oIJdJ4bEZWVblxmvN/0etmKc4yX+xrHSGRVjg/EGYgmKdJYQH44grLK5wMReZf9GSJ+SGt6Aqf84XZIFDptvKmHQ185vbVNJRa2dFUgtNixGqUMBslPBOakZ7pD+X8P5KSOdETRNXgVF+IHn+ctKJNUrS8UpiPSM+CI81zt6xbvvTQlClzJkkglcmlCGXWKrIG9x3qzdlv5189lNN93mMVcZqNfOE1a3n1hjquBqZLNVjMRc0SC4dBFLAa5++x7/AWX7HdNcsxMfuZPdYd5NpV4+knjxy6SDCuF/H//GAPf//GzTnHWiQYtWeqJqS3tQ6F+YdHzqFq0D7PykNCpkgadKMtLit85ZFzOfs8dW6YLY0lHOvyc6RTTzHoDyQ4mVV7MBxO0h+Ms6rKOWb8RJMyP3qpk3hKYe+KcnYuK+X+I908e8FLrcfCF169FrNB4tJQmN+fHMBtM1JfYsUkSexeXjqmQjmRrpEYx3sCrKl2srJqkWTIpuFXk3UqCqLjK3ePjXkTxzcJGF1KvHN34QpjP/nJQ3zvAjz5pdzzzUHfY05cWPzemHkZDiepXuBCp70ryvn98T7yLQmnc81sa3BypFsfjL75nu052y4NRUim9efxG49f5ObVFZQ7zGxtLGHjlx6ddK6vvGFL8R9gDpRYjQxFkthMEmWOyZL9hXCyJ0gsJRN/GdZxvBxIKyo/2d+JP5Zm57JSXJbC5/F8fQFNooDdZEAU9LYwm+o9/PEtqzjeE2BZmY1qt5Vyp3mSsMWOphJO9AbZXO8BwGk2sGdFGU+dG2JTvZtqt5VoUmZjffGp7wvJVWH0KPMlU5GhbZq84BuWuXj80swLuaEJydPhpMo/vXkDt+eRy11icdm0JGAwxkAkXXCDwELo8Ba/upuLP/FkT2586tsHxguI8637Ilny9Ifb/Tk1H/G0MlZzk5iHnmCjaEAkkc75u8cXpWNCrfPGOheyqhJKyESTCoqiomp69/pnzg+xa3kZ9x3qJiWrtA6F2VjvwW42EE7oypGSKDCYkdf93fF+uv0xTvYE6fLFqHZZ2NpYQkpReehEH4mUwqpqFw6LgS0NnrzX/eiZAcIJmXZvlJYKxxWbmz1bsp08zZ9/KMfwyf7V79pQX9T5slkS7c1FKNJx+dG9DXz7Bb3t37/cW8VnfzM45b7fePtWVh3PFcMotxt58k92s+nLz0153KjBA3Dn157j3965g3ZvjD0tZaypcXJpKEKXL4bTYqFzJM6NqypxWvIr/H3q50d5+s9uzbttIfnMHat48Ggfu1eUzVohb5Q1NU46hqOsql7c5qRDoQSDoSSrq688h8tiEk3K+GP6fNLjjxFLyjOKe4DePqHWY8EsCQyHk6QVjeYyO3dtrKLbn2BDrXusDvbaljKubZk+U2nPinL2ZDXEFgSB91zbzHvmkBZ5ObkqjJ7LyUSDx2YUSaTVGZUyrm8pXzJ4rlAWp7T0yuW3R3vm7VyLtR4usRXW5HCUbLeJNOFp3lDn4X17mun2x3jz9gb+7zxc3yjhCXUq+foz3bjCww/39zMUTmA1SkiSgKZoxFMqvlgKTQMlY5X98nAvf/PgWVRNY3tTCTUeK8vK7QRjaQ52+HBZDdiiEqGETDCWJppU2FjvQdMgJWsMR5JEuvy8bsvUhcaldhPhhEyJ3XRZDZ5CJ6M/v62ZLz/esWDXMdEgWmLurKxysHttJW/YXsfH/vdowcd94Z5NlNpNLK9y8dX7pj8uX+bCob+4AwALUEj3GqtJ4ni3vgY41OHj3dc284HrlnGsO8DT54eocllwW6eWNK90zk9fodkSistsX1ZKWtFIK2pRhs9Hrm/hdF+QzVM4Qy4H0aTMfYe6SSuT2xK80vHYTOxaVkq3P8aelnJmqM4YQ9Wgx59AAjbUWvHYLFy/uoIbVlVRYjNxtj/EkU4fa2vdOMwvf5NgUT6hIAgS8B1gLfCkpmnzuc6YV+7eUMUvjk6fT/G27TV85c3bLtMVLbHE3PDH5i9ne2djycw7LQACuQ0kmzwmOgOFfa5nLnl53425wgV3bayZt2vLZqLcfb72TF99soNLwxEiSQWbSaLWZaE/lKDSZabWZcZqkrh3Sx09/hgPHu8jkkyjatAbiGcMGt2YOX0kiNtqxGYysLellAtDMSRRoMRm5K4N1SiqykvtIzSX2se6befjns21DAQTVDhnZ1gWgySMf0fV7sLer1iD53Vry/ntWT3M9jevmrtwxa5y2L84CsVXHSZJ5DvvvYb4LCOpLZ9/aCwKV1zSlk62wWMTITaFF/M3n7yeJ84Mc7IvwO5l42PblgYP62tdGERh2qhVKKFy2788jaxqvG1nAx+7ccUcrrpwlpfZePjUALuWlRQd6dlY7170lCRF0xgVoEzNc1+xlwPZEZbZogDH++JAnGcu+XE83kqJzcRQKIkgwms21fB/XrX2soz7i8limXV3AT2apn1QEIRHBUGo0zStd8aj5ol/fOMG3nJNU0HymVMZPC994Raq3YtUrbpEwRw8vdQkbSJfumc9j39lfs51tK+4ng5zRsot6q/yWKc1ekTG097eu3dyO1ZvJEkwnmb5PKvNFDJtD4YSpBUNATAIGr5YGptRIpyQ2d8RoK7UjsNiYM+KctbWOsd6YdSVWNjc4EYUBI52BRAQaB2OMhRKcLQbPnfHSg51BjjbHyaRVvn07at49vwwoiiwoXbqxY1REmkozd+V+2RvcMq6mGJ4/JM7uO3fD2E2CDz/hdsKOua+j23hLd86Nuv3GjV4AL70yEXec9OqvPvt+9PCGkr//E9zP7/5h5+e9TW9UlBUjb958FSO4E9Bx2W9LiRSM5Hmzz/E6zZX5fzbVAYPwDefuMTR3gCBWJqUrHLtinHVqkKMiYuDERTAbpL4wb6Oy2b0/ORgN2f7g7QOR3nLzsVTjm0djtDhjbKp3lPU4tllMfLaLbX0B+KLGnF6JaA7IFKoaAiq3spiOJxcMnoWiL3AqMXxNLAFyDF6BEH4CPARAMk1f/KhBhFuXzfZq1thg7QmEYjP7IlaSn24enjzj+auxPRyw2Kav8f+7NQp9gvK82cG4A3jf4di06vI3bGhisfPDOKxGtnSkCvrHIil+On+LmRVY+eyuQh4F0ckniIpqxhFAVkVSMgyaOC2GznbH+RHL8qYjSJVTguNpTbWVLuwmSTuXF/DLWuq0DSNNdUuegMx/uSnQwTiacJJmW8900ZDqV1PdYunuWN9Fbevz03BPdDu49JQhGuaS2YtWrD68w9xfo5j4d891okt01fkG09c4FO35jdEsvmbHx6b03vC9Mao6RWQ4nG5uTAY5gf7OvNGOhea3x4vfJD68SE99bfUZqR1cGqllQsD+ZOmFchEg6Dek99xsBB0DOuOjcFQjEg8hcN6+dPskrLCQyf6UVSN/mCCd+0uzvhaVm6/IqWOX25sbfSwqsrJvkteJEnkbTsbWDXP/X+uRBZrdC8DRl3EIWCS21HTtO+gp8Bhrlk5f0Olqjd5mshwDMir+wItFVb+9nUb2bPi6uzdsMQS2bzjuy/N27kWqxniyIR076HI9H7g/a0jyCp4o2kGgnE89vFkmXhaQc6sxsIJeapTLBj+qIzBIBCTVQRBQRQERFFAQKDbF6exxEbrUIRTPUGMkkg4KWMzSwgZEW9BEHji7CC/PtpLlcuCL5YmJStcHIqgqpBSNUrtRn57tA+PzYiGxrJyB8vK7Tx0oo9ALM1INDlro2euBg+A3TxeH+G2FjYdnSpSO+NLr17Flx6+AMCvPj51OvKO//fUkmNrnpFVrSiDp8YM/YswyARiaRrL8hstrUMR3jbNGPr4Z66jdTjGjauKT0WaLV3+OKMdAKxFNidd9xe/J5ZWcZolTv71q2Z9vCQIWIwi0aTyiqgNudo51BngUGcA0Fsr/OZoH75Iil8c7qFjJIqmwfYGN//z4WuRRIFuX4znL3lpLLWxdw5pdovNYt2ZYWB0hnVxGXuqaTClVGs+7vvorpwmiK8UluSsX77E5lGhbLE6UmkT0urzCQRk44+PGzM/fKGVv3/zjrG/a9xWbllTiS+aWpRIjygx1jNMEvVeDGZJXyjKqkabN0okITMUSZJWVcrtZmRFw5UpqE6kZL77XBvJtEJKUbEaRYKygjeSIpwMUO2ycrpP90wPh1MIAqyudvK2axroCyQIJdI4CzQ4Nta5uWdXHX/1+i3z8tmfON1HNPPTnOoYhD3L5+W8+XjfDSt53w0z1/JcPv/8EjOxGAYPQIXDkJOKd6YnxLu+/xK3rinnxtVVpOWpx5vmChfNFa4pty8E2S3PzvQH2Vg/+3EsljlJuMg+MAZJ5G07GxkIJmguKz5SE03K+KIp6kuWygcuF8GEwrFuP+cHQ/ijaRKZ+/tgl5/nLg5z0+pKXrjkZSCYYCCoK765bVMLelzJLJbRcxi4FXgJ2AN863K98Wwe56XF/RIvR+7aUMV35+lcrsURK2J7Y+6k2uiUuOgv7Onu8k+OCi1m/rhBEkipGpoGo2uphKLhj+ppbx6rgUA8jcUoUWE2U+owsqzMzu3rqjLHi1iMEsF4GkVVx3oDCZnmDIFoCkmAlKxilPTUG6MkoqGLB1Q6zWzJ9FyYiZO9Qbz7e/n+/l6e+aMNNDXNrX4gmhVY+8UJH//0jjmdblpWf+EhkploQ6PbwLNfuDPvfkuaUUsMRmTE5LiS66v/XZe8/sXhfjbWunFaDHmNgwbn4su7F2PwzBcuixHXFHLehZBIK/zvS53EUsqUkvpLLAwOs4HlFXaOdwdJyioaYDcbWFutG/ANpTb6gwnKHCZs5uKiiVcCi2X03A/8WBCE54D7NE2bS1P4WTOTgMHrNlbz9Xdun3afJa58vHNonPlyZmuTZ97O1b5IzbtfbM/thNJWoMED0FS6uHnLExvFpdIao0ul7H+PJBUsRpFlFXaOden3si+Woscf42RPCJNB5KbVlayvdbN3eRkPneonJYPHZqTObcEgiqQUlS5/jG5/HFESeN3mWrY2lrCqyslzF73YzQbiaYXb1uUWexfCLd88RetXFq9oeioeONrN0c4Af3Xvxpx/T2Z9uV3B+UljHJ1LNlYveaVfDniAQOZ1/xRlgr881ENfMH8Iqju8CEVLE/jp08/x9psKE+PIRhJBUfWapGI52x+ibTjKtiYPNUUIPcVTylgmwkh0kSaXK5j24QgXBiPzkjrpNgs0lNhpKrPhsBl51foablpdycXBMOFEmpFoiu2NHsqceir43hXlbKh1YzNLRSsEXgksitGjaVoSeNNivPdMvH59BV9dMnheFvQnYWGEiK9unjk7f81JF4uJ0+FMolAmCUaz+m5bV7kQl1QwE5dFHjME0wKqpuWk1GhAS4WTWo+V410BkmmVUCJNJKmgaRqPnxnkWHeQlgoHg5EkDrOBlEHl2pYyPnx9C4e7/Dx/YYj2kSiyopFKqzxzYZikrOK0GNBGmz5rjNUH5eNQhw9vJMm1LbkT7e/eu3CpaMXy9w+d4tvPdQLw00M9nPu7u/LuN50v+l/etHGareO0ZDnPTg4U36R3iSuHqaQL7lxbwR/ODmM1ityxsYYT/ZctI3/WPHYhwdtvmv1xJVYjvliaMkdxkZpEWuEPpwfQNPBFk7y7iGaVJXYTN62uoC+QYNfyxYtYXYkEYin+6sHTJNMqR7rmHicIJjWCAxFODUQQAF84xcF2Hyf7QlwcDBGMK5Q5jNy8upKNdR5ev63uqk1py2ap2iyLpXS2JV4JzCX9YCIbFqmoZ++E9DYDMJ1+23UrK3mxdRi31URL1eL2opiIxWwgkFYmFXpLwO3rK0jJYDBIBBJJLAaJYFzGaTFgMxtJpFXCCZmBYJxwQsZtNbKh1s3XH7/A0a4A4UQKURAwGQRiKRlVNXO2P0xvIM7aaiene4I4rQYePNHLu3Y357x/ty/Gb472cm4gREuFA0XVa3pEO3z+dctZu3btZfuOsmn78qtZ/ucP59324PHxFgOJCXUXHV+5m68+dJoKh4l33Th1bc9nf3mSN+5onPE65q8y7uWPqUjP8AoTXLqMDv+pftNvv3dnzt/ffbaNYOLKvAO+/cFbizpu9/JyunwxVlQWFwk3SiIui5FgPE2Jvfi8Z6fFiMsqYzZcvdGEhSCeUkhlxrRQfG6RalHI7RmnAcd7gxgMEt2+GL5oGlXT8IY1Lg1FKLWb8UaSRUXvrjSWjJ4MSwZP4UyVHrj0HV4dvHRh/iI9vSPzdqrZvW8oty5npkiPP5okmdYIkcKYJ33jUIcPXzTFtS2X34oLJPSrn9jI1GwQ+Mn+HkpsJuwmA84yA+cHw1hNIgZR4MPXNXOsJ8ix7gCJtEqVy4yqQbc/xomeIAPhBGlZxWqSEIC1NS4kUaDXH+NIZ4KHTw4goiEIIqFEmlVVTh441kcirfKpW1dwpCtANCXjj6WJpmRK7LqxfOAvFvc5n8rgAV145rp/0usvdjZ7crZ967HTfP25DgC2lqdYv379Ql3iEhNoLLVRVWGhbXh23XYGRYkrzbz8+f4u4ulZNhy6jPzXUxf42G1rZn2cx2YiqahFe/MlUeDtOxvpD8ZpKlLIwBdN8tlfHMMXSXHXhuqZD3gFUeOx8oG9yzjTH+KNW+v51yLPs7XBRSylcn4wMvZvRgmuXV5GQ6ktY1wpBOIy5Q4Tq6udNJTaqHC8PPr3vKKNnmozvPTXSwv1JV5ZeKdvaTMrFqk1KaEJ/bRKrALe+NT59Cd6gqhALK1xpNNHbcm4RldfIM4z54eQVVC1y5+TH02qiJLAxOVdWtPwRVMIwM5lpZgNIn3BOMFYGrNRJJhQuHlNFZIoMhCM0+uPMRxJ8fT5YZKyjAiYDCKNJdaMuIFAPK0wEEzgj6fRND1/3yCpVDutPHZmkItD+kT4wPE+djaX0TES5YZVFdy+rpLl5Y55b06aXd/kmofi2Poy15TX9ZUnOsZe3/2jDjq+MjejZ32VhdODxbTMfOXR5o0QnaXBA7C6yjkmq3ulsKLKgSgKk70UVwjv3FVX1HEne32c7Y8ULSCgqipfeeQc5wdC3LWhhvfuaZ71OS4NhmkfjqJpGs9e9M58wCuMG1ZVsLnBQ2WRDUR//MEd7F05+/rNlxOvyPihEXj75qolg2eJJebI7kVKuzZMCNaYZxjJstcnT18YyNmWllUeOT3AA8d7OTcwdUPChUJBl6lW0L2lJlH3RlkMEg6zAVEQMEgC5wcjrKt2oaH3EdnfPsLaGifra110+WKc6A3SF4ijabrX1mMz0lRm443bG3jdljq2NHgYiaZIZQqHLEaBEpuRCqeZYCLFlgYPJoOIpsFwJMkLrV52LivlI9cvp6XCiSDkfukf/+H+OX/2v75H90gLwMOf2lPQMfd96No5v+988NBnbuXtm6vwmJei3DNRrC/hDdvrWVFuo8F5efyzu5sc1LiMGEW4qSV/Guz25lK+++78db/ZSV1twxH2t1/+UPjTZ4pzRZ3qiyCrGke6AkUdH4jJnOsPoWnwUltxn7ux1Eal04zVJLGh7vLKfl/ppGSVP/3FcT76o0P86MXOWR+/vcHDXz1wls/ff4KUfGVFTy8nr6hIz9LEtLAs9fa5Otj/hVto/OH8nOuly6q7OM5Ep3FvtPBV1ZpqT87fXb4ovow89IVFMHqyUVRtLNoTSypUuUTu2VJLJJ6m0mkmkkiTSMmowIF2Hz9+qYtDHT7ODYRJyyopRWMkmuJ9e5v4w8l+BsMp9rV6ecfOJjx2E32BOJIgkEirvGFbHSsqHRzvCWKSRFKKxvZGD/5YekzWoNcfR2zJL3Lwtr1zTz95z94W3rO3ZVbHvOV7Lxb1Xh1fuXteo1QAf//2Hfz9vJzp5Y0rT0PwQvjzX52a5yuZno6ROAMR/Qm8OI36Z0rJn942Wn50sifAH//0KGlF5c3b6/n07avn+1KnpKqquIipUYSEDJaJHqUCKXWY2NFcyum+ILeuKU4sptpj4317mznbF+Ld1zbzb0Wd5eXJpcEwR7t0g/aB432zPv5od4Bqt4VTvUE6RmKsmmUz6pcLrwij53/fs5Pr1lUs9mW8olkyiK4cOn3z14lksbJ8J5o4s8n8/9ofzvOhG1aM/R1NysTTKqqqMRK5/DKpEyWsR1HRJap9kRQGSWB5hYN6j5X9HX7SsorFKBFJyggClDlM9AVULIKGKMCTZ4dQgUhC5lRviMfODvKVN26ivsTKLw/3sLLSwR3rq0nKukRsJClTZjfR4RVwW40YJZESu4ndy6eucXrP907RcQVKVk9HIWPN0ng0/wQT8lXR9HXU4AHoncLmOdMf5EsPnJn2POcGwqQzhtGFoei0+843feHiHDfv3tXEmf4Qm5qKF3r53J1zM+580RTD4RTlTgsnp/oBXqE0lNmodlsJxFJsbvDw4CyPVwFRFFhd46Sp9Gp4GheGl63RszRxXT3M1DcpH0u/b3E0lFio8cyfAsuX7p19wex88KrVJTl/f/s92/jQ/xwBYE25ZdpjnbZcT2ZzuZ1aj5W0rLCxwc1v5vVKZ+b2NSXs7wghqxqqppGSNTTAYZaoclmwmSTu2ljDhjo30aTM/nYfg6EE92yuodRupsJp5p4ttRhFka8/cRFZUVlR6aQvEGPAlMRqksYmuSqXhU/cPG7wmQ0Sb9hWD+iSs3rES+FV62uuWHlSG/PfQHS+x5MVnnk93RIz0FJuo9U7+a749K0r+eTNyzEY9KXOTHNNhWVyFHkiqbSa10kB8JG9uurf6zbVsq91hJFokk/eMrtI5lzxDRd33Ks21dJQbl/UpqB2s6Q3fk3IVLunH8dfaTgtRr797m10eGNsa/Twd7M8/sYVZXz/A7v0erRXMFe10fP0Z6+nuWIp7/OVyHxEjkSgwmkgFJNxmA0Mx+SxiIHHBCtqPbgsBjZUu1hZ4+SPf3ocAIcEb9rVyCMnB4inZMptAoN+BZcd+jJOvUojvPv6Zfz65ABdw3EQoKXcjtkosK2xhJZyB6vqXJzuCfLrIz2c7I9gM8Jnbl/Dfz9zjv4JzkEjuiTzTSvdOMwWWiod3Hewi2BcpsYp0erXJSxLLZBUBawGkWBcIa1BncvMsnIblS4Ln7ljNQ0luV6eid9XdhrQf71/3aTvbW+zmxc6gpRYJN6+u2XKY988gz003fv+fzfVT9r/zVsq+cWxIawSfOv9ufUft62r4cAXbqQ3kGJr0+RCoz1NTvZ16h7QF7/46pxta2rcfOUNG+nxx3nV+ir+dvrL5p61pTzfHsCfyJ/i8trNVVwaCHHtsnJeu62Bh0/10ToYocptw2wQsJmMRJIJHj45yBt31LF7eSUfuF4gkEjjMEmc7w9jMohsqPdQ4dDz28syyjl2s4E/e9VqhiNJVlQ4MEyQAt7RVMKZ/hBNZXZKbEZezOTWT+yxkw+LUeLerYUXQWf/ftm/3Qftk/cb3XbHyuKKwLLf60zW+a5UHv/84jhlOq6Q72bidViKkB/+6ps38vrtjTkpiTv/7lGGIuNKLO/aWctfvnYzJ3uDlNlNNJfbCcZSvP8Hz/DLj92KKOa+72duWcZ/PtPO39yzlrfuHu8z9fy5PjbVOnC5XGPvZ5siS2xLYwl/dGMLZ/qDnD3ejTcB//Dha1hbZsLj8QBgMkl89a1bZv2ZizW+RcYVLD9wS3GOqO1NJWxvKpl5xwXEbJB41+4mQvE0la5co2fJ0QnlDgvlDv172VjnZiqpBxH48PXLeNOOelZWLa2RsxG0RVArmi2e0jItbp79ZCkAoiBQ5jCRlBXSikapzYQ3ksQoiTgtRtKKijeSRENXMlpe4aAvoDeaq3SakUQBWdWQFY1YSkZWNUpspkxzPz231ySJTKjxJSmrJNIKSVlPm3FZjdhMU+faapquHCXN0gpvb+/AUlqdSdEpLMGn1GYkJatEMt0abUZpzKvri6ZQMgLuRkkYk+Y0igIuq5GRjJpUudNMtcuCP5oiqaiQuX631YhBEgnF02hAJJEmKatUOM1oGgyGEthMEhVOC0lZwSiJxJIyvmiKMoc5x7sTSynIiordbJj19wLQ0dFBc3PzrI97JXD0zEUMbj3vemNdbjqDN5ygP6R3HLcZRVoqc3N/B4IJfNEUNpNEc3nuCjeSSNM+ontc3WYDjRO2Z6csrK9x5XidNA1O9enbGzxWPBN6PYTiafqCCRxmifqSyeH5QDyNrKiU2EyT7hd/LMVwOInFKNE4Q2g/+7uZiNtiwG42MBROIk9srANIgkCdxwoCGCQBi0FCFAUURcMgCYxEUsTSMg6TAUXTsJkM2EwSibSCqmlYjQZUTUNRNUxXYJ+K1rZ2rKXVOMwGXNbxSFBaUXNEILLvqUgyTXvGC+80G3LumW5fjEBcX8Q2ldpyzpl9r6ytceV0is/eNvH+nY5EWkEQhEk9QDq8EcJJfTzcUOuaJNqQD1XT6PbFSckKVW4LbW0dSFM8U5eTYr+b+SSZVriQUQIUAEfaPzYW+yIJeoPJsX2tRhGL0YA/Np5aKgkCVS4zZQ4zw+EkRknAYzPR448RjKczc65AUlYQEGgus5FWNbzhBLEsOWlJFFhWZqc/GCOa0v99XY2LS0MRFFVD0zQEAawmAwZJf3bFAn57AH80RU9mrVDlMlPpLC4ykT3eNJVYcNkWJ2lYA2RFxVhkT6WFIPu7qXdbKHmZyCbPB5fa2plpXVxmN1HlsuCLppBVFVnR15hGg4iqapgNIh5b8T2VZkLTIJxMIwoCDvPli7EcPnxY0zQt7418VRg9zvrVWtm7ilUln5rdjU5e6srNf63zWBgOJ7CZDCwrd+CNJPXFuSgwlFk0bWn08KMP7uYn+7sYDCWodlt4+049rJ2UFQ62+3j6/DDnBkJcGgqTkjVaKh383es2srpmcvFYLCXzvy91Ek0q3Li6gm2NhXtbSpvW4Hr7v8zti0CfmCQB5AJvBxG4aVUpT14Yr2SXAKdFosptxW010uWLMRjSDUpJhOzazwa3RH2ZG0EQ2Nc6rvTiMcGt62r5wt1r+dFLXQAsK7fP6IG+NBjmu8+3U2Yz8cHrl1HmMLNjxw4OHTpU2Ad6hWGuWUnNe78GwEoDPPZ34160iZ7iiR627O2PfOo61tSOL6yWf/6hnJ450x27s97CfZ8cb6S37s8fIpZ18MRjW77w0JgK23+/Zxu3rKsZ2/Zi6wjv+q+XUFW4bV0l333PNTnHXv8PTzAQSiIJAt9/7zVcu3LqyEf2d5MPlwFC0/SGK7UZUDUosZl4yzUNYwIJG+rcPHNBzz25NBzBIAiU2k3cvbGG3x7vo8ppZndLGWf6QtjNEg2ldm5bW8W62nFPnaZpBONp+oMJHj8zSIXTzBu31xOIpekcibKyyok7y3C4MBim1x9nW2PJvKSrOetXU/Huf8Vmknjuz24Za0I43T0z3T2RfZzNKHDmb1+dd1utDfb9pX7cu775CM93jjt4CvUA//ildr0WQxD47ru2cdPacSGG7Pfa3uDk/k/cMOP5fvRiG3/x27MA2E0il777qbH75sO76/jivVsKuq75ZKZnd7Guo/zxv+LQoUP8+kAbn/nV2YLPc++WWp44O4ggCHz2thb+6nfn8+7nMQsgSgTyNG20m8Qxg2c6yuxG/uMd29nU4MYbTlFXYp3W2bbq8w+RXQF4/8d2s7159n2+Jo43xfxmrcNh7j/cw21rq9lWZLTmSw+c5mx/iM0Nbv781ZMj/IvBfHw3L1csNSupnmaeGsUsQca/jVEScFmMLKuws3t5GaIg8KHrl9HnT1DiMOKNJDjeFeK6lWU0lOY6LIfDSQ52+KgvsbKp3jPj+6qqxqNnBjjdF0IUBO7aWM2a6ssTdRIE4bCmaTvybbsq0tumUkqZKxMNHoD+QAIVSMVljnYHcJol4illzBhIJxXO9YUACMT1IS8QS/PAsV6+/0IHFU4zyyvsvNDqpXskSkJWMRt0T64vlgR0oyeRVugYiVLrsdLjj3OiJ4jTbKAns0AplFhKYT5uI43CDR7QQ+nZBg/oaWGBhIJKHJfFiMNiYDATMVAn/IRDIYUqj4bVlGuMB1Lw6+N9fPCGFsxGkeFQkkRGXvEn+7u4/0gPr9lYzbt3N2PI8tb+/FA3l4bC7AslGQwneP/eZYV/mFc4F+fQ3Ll1MJRj9MzmSe0Zyk2ej81wcLbsdOtQhFuy5uV/euTMmFH9xNnJzVeDcZm0oiGjYTDOzdEzncED4IvpPXJiKYWnzw8zGIwTTyv0+KM0lNo51RsiEE2hajASTfHEuQFiKZn2ERmTQSCYkBnpTxJLqaiaRkOpFadFN1geOTXAuYEwQ+EElU4L/cEEg6EEDx7vJ5FWODcQ5l27dXGBUCLNwyf70TQ9gvvG7ZNTBmdLSlFRNYgkFfr90YI6rxd6T0znCOzLKtcwhqaPaB/q8OGPpbm2pSzHu/j3D59FDwJofOHXJ3hxbX71uQtDhRWCP3t+vHhi4qL69MAiyRpeocRSCv/yyDn+7enWWR3ni+pzrKZp7D/TMeV+gaQG5H8w5QLXDyPRNM9fGOSfHz1Pjz/GHeuq+Jt7N+bsc6o3yPMXvdywupyJkid/dv9Jvv62LWyo8xT0fvPJW7/1Ir5Ymh/s6+T4X96O0TB7BbdR5cpz/ZEZ9lxiMVBVjd+fGqDbH+Om1RVT1pVNJJk1XKYUDW80hTeaonMkyp4V5Xz9iYuc7Anitho42RskkpApc5h507Z6tjR62LW8DKMk8tS5IXoDcc4PhGkut+OyTO9Ee/BEH/vbRhiJpFhX685xxi0mV4XRczmZODyGk5Mn2OFompv/8UkGw0kqXWa+8sZNfPa+44Tiac4NhKnzWPTGf5KIJKu4LAZuX1vF1ixj5ncn+un2xXCYDXhsRkT03hhr80SCpuNKjNOFEgrn+v3UldpxWiSiSQVBy52SRINEtcvEpcHJA6ymQbndQDSe4tHT/Tx6ZoBUSuHrT14imVY40RPg8bNDfOC6Zdy6tgp/JIXLaiAcTxNNyogC9GbSDmDq+p8lr5HOXFpCTkwTmg3CHN64YULa3PJyG0e6dWdEPsVViyQQRo9mqsrCF3Kq6L13ArEU3kiShKwhigKfum0lRkkYU9CTVQ2nxThm4IQTMgPBBEZJxCgJWIzSWJpbMJbmD2cGEBBwmg2YDSJVLguVWSkf2eOBSRIxSiIpWUUS4UxfiMYy27ylGexrHWJd/fzVAMjpwkazyioz+JN5t/X4Yzx30Yum6alLd6wfN2z0+g59PJemufluW1NY877b11bx2Dk9q9444XTDw/E8R7xyiacV+gOzl5/48us28PGfHaXUZuTmVQYebhuY+aAJ5JnCp2QwnOBsfxBVg0fPDvIX96znYIcPs0FiW6OHf370PLFkmvsOdU86tmskSvtwZFGMHm9UTxGNpRS8kRg1ntnLEb9mcw3PXxrhltVLSrdXIsF4mguDumF6rMheStkMhVMc7vCxotIBgDeSIpHW56HhcJLfnx7gdH+Igx1+rl9ZTondRG8gjt0sFTTv9/jjVDgtOCxG3rm7kfIrJDVxyeiZgdEC8om0+/RJrWMkzu+O9eG0GAnF09hMIntWlFFqN/Gjl7rQNLhxdQXv3bsMS9bMGEvpJkAirRCMw1AkSanNRI1r/pS1FpNwCs4N5JfqFAU9H35/+wjDkcneufU1dj7+06Mc6QigAgYRvvlsG6IgoGTSMaPxJC9c8rKm2sX7vn+AUDxNSpFJplWeveDlw9cvn3TeJfIzlzZlNqn4o2drelgNAvFMOLLGlTuAOi3jEYd8Rk9zpYNITxCryUDVZXjGhMx/iqrpjUdVjWRa4bvPtHN+cDySYBDh1rVVtHtj9PhixGWFSqeFLQ1umsrsvGF7PeaM1/bFthHsJgP9wQR3rKviro3j6X1v3F5HhzfG6qzeCxajxBu21fGbo708fWGY8wNhKp0Wyp1mBoJxrm0pZ0Oemg9N03ji7BD9wTg3rqqksSx/DdSwLzAv39Uo4QIjjm1D+Q0e0NM3TvcGiaaUSfVmKysdHO7S6112TJDlNUvji+NdywvrMXIiE/EH0LTcm+7i5VUpvuKxGSVGIrM3BA92+7EZJdKqht3sWIAry6XUbiSlqKQVKEHgcKef/W161M5pMWCSRLwplZHI5HswrcIvD3Zyz5a5R1TnglJk5P4du5p4x67Fl6DXNH3MvBJrGhcTl9VIfYmVvkCCtTXzkyY2Ek1iHBEpc5i5d2stPb44L7aOYM3Un3vDCRxmA89d9PL2nQ2sqXZSajeNzUnTcfPqSk72BthQ575iDB5YMnpmpMIBg9Hc1JqJHO8O8JHrm/nXRy9y85oKbl9Xw0g0RbnDRCwl0zoc5UcvdvCh65ePGT53bajhVG+Q5RV2XmobYXWVE6tJIiErOF7mP4uq6UIPkSnUr96+q4l/e7I1Z1Fc7TZz86oKBsMJfrK/h2N9EU4NRNi9vJRoUiYpK4QTCkZJQFZ1b8USC88jZ73sWddY1LH+Wa6BHBYj8UgKgwgGMXfQPdodGHudyGOHNZXaONcfwmkx4LFfnudLkgQ2N3joDcYxotf7nRkII8sKBgHMRpEyh17T8/CpAVRNYygUJyWrlDvNvH5rfU4KQaXLTLnDTK3Hyp4VuTVJlU5L3kLqoVCSRFplJJJCRGAonKDHn0ASdZGStTUuTvcFQYP1dW4kUcAbSY0Vw+9vH5nS6PGl5mIuF09DqYWDvvy6wvGUSkulg6SsTvJGnugeL/B/4nyu7tHOZWU8d2kEm0nitrWFGT3NWYIYE9dnM2vlvbKwmiSuXV7OU5cCszquL5BAA1Jpmd+d7l+Qa8vmkVODjOoBDYYSWLMclSLwf1+zlkdO9fPjl7oJJiZbF4e6Fr+3TDqdz017dSArKvcd6mEonODGVUsRp2wkUeDNOxoy4hsC82ESyoreHmHnslI21Hl4z7V6WUCPP8aFgQj9oTiDwQQWo4TTYsQ+iyyBdbWunHrUK4UlU3oGrl1Ry/KKXI+hwyQiZa3IL3mjfPnh8wQTaR443k+vP8ZQKIXVKBFPq9iMImlFpTurKWSF08zNayppKrOzpaGEareVtTUuShdQSeNKIynr36XDLPHazTWM1ln/9YNnee/uOqrcFjbUuvjTO1dz75Z6/LE0HqtpLIVHVmFTtZVVVQ5SsoKiQULWsBhEVl+mgrlXOstKir9fy2bpuA1m1J1kFQwTwjk3rhwvIC5zTM4dvjAYQdUgnJDpnq21VQSjapBOs4EapwVRAJNBRFHVsdq5eFolFJexmyX2tpTzmVtXMBxJ4Yum2HdpZJLwwLbGEt65q5H3XNtUcH70YCjBxcEwDSVWdi8vY2OdB4/ViKJqVLvMfP3xC/zd787w3efb2N+uC4q4rUbKHPrvurzCgZpHpQ5gY+38LkqsBc5GbYNTN1Kp9VhpLLXisRnZOCHNyGQcfwP7hFrCw536Z4+lFM72F7ZwlbMcKxODi8Yrx7F5xfC6WdaUGSVwmA00ldkIxRUePrfw4bPs50rRYHODh9duqWV1tZMHT/Tz7AUvb9jWwD2ba/Iev6tFT/dUVI3Hzgxy/+Ee/NHL2/A4OZ2H9gonEE8zGEqgaXAxT+r7EowpS84ltXwUURCozjjMWir0CbndG83MPwa2Nniwmw3saNL/f7jTx/dfaOdwp3/O771YvLxDCvNAlcfCZhQuZnVV/smHdvOR/z3MwFiRvkZa1RuWSZKAzWhgU72Lbz/TSjytoCoqBkmi3GmmvsQ2FjocZXW1k9XVhefgRpIyibRyRYUMi0FFj/jUl1pZUenk4ZO6Jy+laDxwfJB9XxhX9vrus62c6Q8jiQIGUV/4WiSBqlI3//imzez5+yfGz6uqBUnOLjF3Hj/bw/tv2VDUsV2znNOya8WfOTvM6urxFKX1tZ6x1zV5mtoF4yliSYWUrGK8TM3Z4mmVJ88NMhJNo2oQS6qYDCKaCHFFQwSiSZn/ePISF4YirKl2EojpedWjMs4TqXRZ6ByJcqYvxMZ6NzbT5CE8kpQZDCVwWQyc7guxrNxOjdvKW65pIBhLU+Ox4rHq6bg/2d+FP5bGYpTGxEZMBpF37moiJau8cMnL15+4yJYGDzevyY2AbJqHJoZWA4wKbq2vL0xeWZhmrg8lUjx9fphwUmZ1tTMnSrWs1MqpTMrtprpcp0gs6+v+6uPnuH71zHU9Spbyy0QV1P6pM/BesSTk2UUgFAXO9YdIySr+ePGGQ7XLxEBo5uMdZpHKPMIcLRUODnfoi7zhcBIBeOs1jfz7U5NFGZKZt+kciXIqEy092OHLqS1baCLJGbqrXsGUWI2EEzIdI1E21C05LieiqBqJtILdbCAuq3jmeL4Kp5mdy8tYWekYM/ifPj/E4U4/j5zqzzjuFdq9UbY1lvDCRS+KBi+2ehe9p1OxLBk902BAL+7a35Hr+Xvdf+7jG2/fwud+cYykrN84b9pWzyOnB9jZXIrHbiSa0A0TTdNVYYbCCXr9cdq90bGQX1pReeTUAOGEzB3rqwoyYvzRFD850EVKVrltbWEFt4tFdsO0qUir0OtP8J9PXcqRtC53WlDU8b5FsZRCfYmVcELmmuZSNA1uW1eFIAhUOM056Yev37a4OdWvJCyG6XveLBQrJ0TyfnmkZ+z1hTwewmhSRkUP50fz5b8tECPRlN7rSgOTQUBD99DF0yoqoKnw6JlBQgmZQ51+NtS4MRoErmkuJZJI45igkBNKpPnN0T49FS6c5J7NtYQS430QZEXlp/u7iCRlmsv0vjeheJqGzOLfbTNyZ2YB9tMDXayodNLlj3HXxhp2LR/v+SCJAlaTxJl+vW7lTH9oktGTHTkpluwMoVZvYVbw9hWVHD08WaEP4MJAhFDmpEc6/dy1YdwjH85KxxuJTV34MDhS2HVowvj5JjrXK5Z8LpMIJ2enwqqiCyA4LQYEQUBg9sI9BhE2NngYPD0047HxlEpDmRUu5v77YCjBqioH8bQy1keuzpO/LtBj05dUZQ4z0aRMOCFzy5rC0iXnC7PJPvNOebg0FOalNh/Xryynqay4c8wVfzyN02JgY50bf/TqTdNbCGRF5acHu/GGk+xpmb00ej5GIkl+drCLh0/28aMP7KLaY8UoiQyHk8RSMsF4Gn8sTbnDxN/+7iy9gRgmg8irNlw+I75Qnj4/RLcvxt4V0ycXLxk90yADsqpO6nquoTfL29xYiqyo1HlstI/EkFWNF1pH+NYzlzAZRKRMU0KzQcAfSxNOyNSVjA+WnSNRLg1FuP9wD1/89UmsRomX/s/NuKYxfvyxFClZnzwGQ1euR8dmFHOaxAnAjavKePbCCGrmb0HQ63tSisbodFbpMLK+zs2m+hIiSXnM+3DT6kpO9QVZUeHg2YvDXBqM8FLbCPUlVpZlFSxLAnzm9uI6Ui8xe65pmX3T4FFKZzn6NJZY6fLHMQiwujp3Uq50jHtoRWHy8iaWWfCqQCh5+dJNTJJIWlZJZW5xt8VIODm+4NYAm9lAICZjlkQ21rswSSJPnR9iX+sIf/GadazMEicYiSTpHInisuoNe493+/nXxy4iCfD5V6/BZTESiKcwiCKxtMI7dzUSSqSpyDOmXLeiHFGAN26v59opJtEdTSWc6A2yOU9fhpM9QdbVzW3yzf6lCjWhhsOhKbe5LAYOtvtQAasx1/JQNTXv64kkC7yS3sD4omxiBuCaZYuzaLxSUVWNwx2BWR/3f+5aw/HuAAfafaiKwnB0dlX6sgrn+kIYREjPYHMpGqiChE2CmALb6l2c7Q/xyKkBREHgrdc0sK/Vy/+82ElLZf7c3KFM9sdIJInZKGIQjYQSl3fx3lRSXJPUT//sKL3+OPcfcfDrj++d56sqjBKbicZSG32B+BVZD7KYhBMyfYE48ZRC23B01kJA+YilNdJKkkRaYV/beOrmUChBly9G23CEtKw3rB2JJggnZCpd5iuqgS3oynZHM4p2L7VN3y5gyeiZwERv0hNnBvXV+QT+9ndnOdcfwmUxIqugqXrTwERa5TvPtiGJAisrHfhjKexmAy0VDt47IRe/0mXBbpboD8bRgFha4X8OdPHJW1bywiUvrcMRdi0ry0l9ay6zs6XRo0c8lhW/4FxoYnlmmIMdfiwmEVUFkyTgtEj0BnMXoH9y6yoUDfqCcX64r4MbVlWwpcHD5sx/sqzy6yM9HOjwUe2y8PSFYR460Y9RElBUjetWlGNcUn25bBjF4nWnfbNUGRoM60a+rMFwKEltSZaxm/Wb282TU1TiWblxw+HLJye8tamEJ8/p/VwSikY4KVPjthBNRMZU81ZWOIgmZVxWI7UeG7891ke7N0qly8Kp3lCO0fPE2SEGQwkuDcm8emMNhzr9JDKV14+cGkDV9KjWhjo3N6yqwGKUCMTS9PjjNJTmRuUaSm28tXR6EYo9K8oniSaMcrY7ALuK+15GkRhXDywvoOcPQH1FCVzIX9T+H09fGosuH5ywyO72j481p3un7sUTnKlhVIa7NlTx04O6dLHLnPscDA0vybdl0+WP8eUHCm9KOoou5CFw75Y6fnvgIsV8rf2BRME96A5eGhxT8Wsdjo71CVI1DX8sRedIjFBCbwScj0NdQXZ/+XE0DardFm5afXmjPAAxWS6qd9+5gQiyqhHpXTwxBkkU5qWf2MsRs0HkcIePoXASm6n4eTd7jTu6srUapTFFOLfVxMdvXkEipXC4y89T54epceuGzr5LI1S5ijOqpyMYS/PI6X6MksirN9bkKB0XgsNsoNJlZiiUzHGC52PJ6JnARM+jhoYsgzHLU9TgMdEXiFPjsmAxSWyqdyMCL7UrDIWSjJYMmAwS62rcJBWFNdUumstzvUMui5H3713G7070cbI3hEkUeOuORuIphQPturX6Yqs3x+gRRYGbF2EgnSsaegM/h1nCZTPofTQQEMXxxqXVThObG0uodpv5/P0nSaQVArEUbcMRblxVQZnDzKFOHyf7goiCMJbW89zFYRAEnBYD/+8NG6e9jiXmGenyFc0ms1YuZwYCbG4aN/r3rqjghy/qC9B8qSdC1kgfT12eazaKsLelnOcuDI+NHSZRT8fsGImiyBpGEQbDSUrtZsxGkTU1Tn5xWMn07EmzrDzXUFE1vbeP22ak3RvltrVVnOgJkpIVvYmyquG2mlhZ5eTSYIQT3UEOtPtQNI03ba+fUeo0kVZQVK0glZ4bWjzFfjVjZCcadhYoMJGeRjXutZtqeOTUIBpMEqAplDs2FCbQ8MT58RS72IRV9fnC+pu+YpAVbYrWodNzsMPH2f4QD5/so81fXJPy2TTdPjecHDOag0mF7U0lRJMyZqPEqionPz3QxbHuADub8zscNfSGqmV2E5GkzI2rK9iURxZ+ITncNsTd22Yv7y1l5mLDZap5XGJ2DEeSCIJAlcvCYDhRdI9GSRSQM6Fph1lic72btbUuPQ07g8UoYTFK3Lq2iluzyihetb6G4UiC9bXze0+f6gvSF9CdmhcHI2wssL5zFEkUePs1jSRkJW+dazZXhdGzWI+gKArIika5w4QomvBF01Q4zTSV2RAQqPNY+OiNK+gPxtnf7uPa5eUE4ymO9wSodFqwGkRavRFuWV2Jhp7Olu21BTBKIg988nqSySRms56ComkadR4rvYE4yysWvjdBscyUYy0CFqNALKvpYInVqEe1Uir+WJoSi4FSq4GGMjudvgT/7+GzvGZ9FUc6AyTSCmf7wxzv8vPtZ1ppqXSgaiq9/jgqcH1zGVsa3DxzfghVk1ld7aC+ZHFqTF6pROLFV2x75vBgl0yodfnN0fGanuw+OKNsbvBwtCuA1Shy9+ba4t+4QAR0JbGjXb6c1KeRWJqBoK5OBHoj3g11Lk70hNhY66auxEqVy0IspdBcaueZC15WVDnHGoouL7fxnEGkzG5mR3MpTWV2/vUtW/jm05c41x9mKJzgHbuaGA4lOdEToGMkyvmBMAZJZE21c1qjxxtJ8vOD3ciKxmu31M7oMXu+PcgtW+f6TY1jK3A2euRg/noegHV1HnY2lxCKJ3nLNblRrDvWVfLoGf3YD+5pyNlmFGB0mHr91rqCruPezXX8+KUuFBVWVzroKOzyX5HUuC1sbHJzoHN2UQS31UiPP0bnSGxO/cQK5eYVpTxxSXc4GgR98ZctQtA2HMVqlGgdnrruq6nMhqbB67bUsa3x8hd7X7uquEjJlgYPFwbCs15wLnF5aCixccPKCi4Mhnnjtnr+fZbHj+ZCaJouoiOJAom0wqHOAEe6A4TiCmuqndMaDY1ltilbGMyFxlIbRzr9SJJArae4SJIoCjMaPHCVGD3zTUu5lU5fHHkGx5GsapiNEjubSih1WnjuohdJBEXRuHZFGW+/phG3zUhdiZUdzaXcf7iHLl+MSpeF4WCSXx3rRQN+Furi07etHiuCBL05aetQlIZSKx6baczgAV2S8E3b64lnVDquVEY784bi6bz50lsbXBzuHs+/d1skDJJA+8i4R3ckJjMSk2n3J6hyWejzx3j8/BAaGhoqibReDB5JKbjDSfqDCVZUOhEFgb+9dyOBWJoXmkZQNI3P3LbqcnzsJbJYU+kp+tgC+ptNiS+WmxZpyvJOaurkm/G1m2updVtxWw2I08l/zSOxtEIons4xegyiXl+UylS+yxpYDRItFXZsZgPVLit/ec96HjzWRzQlYzQIOZ7X1uHo2EKqOVNsLADRpMJwJInTYkQQ4OkLQ7QORZBEgUAsjSAIeKMJWocjPHVuiCqXhbs31iBmnXsgmBirF+zxx2Y0et66u2Ha7bNlitY7k6hwQdcUZT2DoTjnB8PIqsbhDh/v3j3ebNFjM2I2CAgIiMbcVLosvwy/P97HbetnNny2NpXys49cS6s3zN0bain7+/FtdYUpir9i6PHHic/S4AH9WfHYTDktIhaSlKbiMhuIJGXW5lFU3dzgpnUoytoaF0+dH560vanExGP/302X4UqnxlakwIjdZKDMYS5o4bjE5UcUBT5xy4qijx+bFUcdbqqGAIgiCAhcGAxzaShCc7k9pz/c5aCh1MaHb1iOKAgL3pT2qri75zsZxWI04DJL+OKTfUfZOeYAsqxwaSSGMZDg7IDuQbYawnzvfdfkFHN1jcSIpxXKHCZuXl3JD55vH7vuRFqlxGbM+TF/d7yf3kAcm0niQ9cvH1MpG0UUhSva4BGAhhIrHSOxvAbPxjonG+pLcoyetKrlGDzZKKpeqCcrKmgay8qsXByO4rYasZgMNJU7SMkqt6+rwhdN0VhqY3/bCL851ks0KbNzWdmUxaVLLByCsfjBcZY1yTmE4rkH+7M0h/NlP22sd3OmP8TySgeeAnvczAUNCMXThBLpnPGrxm1hba2LvuD4Cj8hKzgsBtqGIzx0op9b11byweuX0Tococppyclv3tLg4eGT/bhtRiIJGbfNiCgKvGNXI9GUjMNsQNOg1GZCq3CAqjEcTuqqcZLE0a4A4YRMOBHBG0lSmZWfvbLKQbs3SkpW2VyAHHVoiubCC01lhQFC+W+eXn8Ck0HEoOrffzYemwlB0FMdy6apH2rzFV7ztaO5lB15Up16l4Snckgpxd0rZoOIw2yYUYRgOhwmkUiqsBOEM8+U1SRhzlM38YmbV9CbqY/76uMXJm0vz9Mg+HLzUtsgN60tLFqZzbEuP4G4THAO8uBLXD7m6gcQALfFgMtuIp5SWF/r4sXWEZ44O8SeljJ2LZ8fhbhCmW0dT7FcuavqBUTVNIJTyNaKkq64NsqKSged3ij+rEVWXNaNnGcvDmE1SdS4rPzgxQ5sRgMrKh2UO8z8xWvX88T5IfoCcTbUuej2x+kaiY2ltyUzk4CsapkeD1dfHm3bUIRRFVKLBBaTRDihYDeLVDrNlDpMOSlwsSkmHhE9/15WVIYjSWQN7tlcx2Nnh1BUjQqnma+9ZQvffOoC//FMO1ajyJt3NPDro7280DqCw6x3C17i8hOMFi8KMJd0FWGCkyA78pPvLnvuopdz/SGGQkneuK0ez+VoAqzpAgqjz4AANJU7SKRz3Th3rqvgWE+EA+0+fry/k1KbkWtXlLOm2kUwlub+wz3YzRK3ra1idbWTfa16I82nzg9xbyYVq6HUxufuXE08reCyGHngeB9lDjM7l5VgNErIisqta6oIJ2V6/DEqnRbcViNdIzHKHCbsZgNmg8Q9s0n9m0YBrRhqHIWNgUPhqa3luzfU8PjZIYbCCT56U0vONrNBRBIEBEHAPE2YUVEKt8ZHZYmrJ/SGanlFzqxTU2ydSLXbTJs3gqQVVxMEuoLizM0TdDw2I1aziS5fjC15DH+byTApRT2bYlNz5pM9K4uTE/Zl1jjeyJLFfjUw144BJoOIx25kWYWdnc0lXNNcyn+/0Emp3UinL3bZjZ7LxStyaB4MJSb1VRjFbBBJK+PLsRtWlfO/L/VM2u+fHz3HC5dGMBtE9qwow24yMBxOsq3RTYc3SpXbwv932yqeb/VSYjPitBioySqwvntjDWf6QiyrsE+SxL4a0MhdtKYV8BhEEgYNQRDZvbyccwNhXBYDwcT009WGWifr6zwc7PDhtBhZVm7nzTvq2bW8jBcuedm1rBSDQeQbT7eRVjQSssrvT/ZjNkooigoY2N505SrZvZy5OE1u+0zMxcy3TgiBN5dYONmnX4s5z3r2hUteunwxBoIJRiLJSUbPxcEwvmiqoAhHIUgCGCWBQDxNc5mF7pEENrPIUCjBdSvKeebCeGqMxWigyx+l1x9HEODnh7ppzYgUnO0P0eWLAbC8wkF9iRWTQSQlq5MiwU6Lccz4f8uO8dSzj99kRlY1ajPjz9oaF5Io8OjpAU73hXCYDbx3T/Os0woOdwa4pmX++jUMRgqL6S8vgSna9GAySfzHO7fl3fbkmUGiGcfLM+cGeGdW6ls28WRhy+toUuYzPz9Grz/Gx2/ONbC65xDFfDmytsbFTz6zhzu/um9WmRvv++8DtA7H5pTt4YsX/mO8ZUcj//zYRWIZWeCJpBVVb1A6hcF/qGPxO9UPhGM0lhTe7HyU0abfS0IGVwcFBi+nxGES6RqJ0x+M0+WNcWkoglES6A8meP/e/LVoz14Y5vGzg6yvcXHvtrppnUdXKotq9AiCcA3wbWCPpmmXremMb5rGdBPTzJ67MEKly0R0WM7xFY1E02gaJGUVp9lIjcfCzasrMBslHjk9QLtXz50XBIGN9W5+9IG1OfnzVqOEx2bEPgfpwcUmuyZKAbzhNJJBwGqUqPfY6ByJ0VJp52x/GFnRSE9sZpHh3ECY1uEYZU4TJoPILasrsJmM2ExpTvWFON0X4ta1cWxGiWDGC1tqN9HljyMrKh6rkTctyVwuCss8xacUzqVp/ckJsqrlrnGHgs00OernsRkxiAJWkzjpGR8KJfjdCV0COTyDgV4okiiQUjRMqobNbMJmkYlmoiydvmhOBPTJ88MMhVIYRAGLUWIglKDdG+Vwp58VlQ6O9wQwGUQqMvn279zViDeSmrHmBqA/GOcXh3pQNY17NtfSUuEY+/z+THQsmpJJysqsjR6DMJdfcDKFLmxraurh/GRH1EycHhg30J9v9U65X6F9VR49PcAT54bQVI0vP3wuZ9tS3Hky9S77rBuMdvvjBe9f5TQxGJ5batYDx3sYCieRFY2LQ5MdOvcf7uHiQIgHTw7kPX4wtPhRkrDfB0UYPX/3ug3cd6ibd+1unv+LWuKKw5tZBysydPqiWEwS1zSXsrHeM1Yvmk0onuYbT1ykPxjnUIePcqc5R9ntamHRjB5BEJqAz1J4T7oFRwDCWV4hAbgwFMFhlnIMntdsqOLVm2v5tycuUWI38YmbV1DhMPHcpRG6fbp3yBdNEU8raJoeWRo1eGRF5cW2EZ4+P4zNJOEwG/jQ9cv55z+c4c41FWxZdvXJUY8iA5qiEY6n+K/n2wjGUvQGE3z4umVEUgo/2985SdoVdI9FSlVI+OJYjPB86wirq908c3GIc/0hwgmZtuEI25s8vNTmw2UWCSflTH8SvXP8lVz/9HImphafpDZ93+TpWTmhyPi29TX85EA3aQVuWJk/LB9NKQiiOKaENookCgiCrqQmzVPFtEkSEUWB1VUOLEYjvb4oGnp934WBCLaMqqFZEkimNRpKrQRiSTRNQEOPPL1xWz2NpTbetL2ecod5LOe5P5hgIJigwmnO6fuVD284hZJxNgyFkrRkqUHevKaSQx1+GkttRaWHKtr8pvLYC3yEV5VPv6B75twgJ3sCfPK21VPuM51dI0iFOaJiCTkjvQ/JCUUnl68T1NVDuzdSYJLZOMlZ6E2bpzDazRJjvXdm4khHAEVWSCi54iijDIeT+GLpsd5YE5l4RCCW4qGTev+RezbVYr0MTs6LPli/fPbHvW1XE2/blT/6ebnQNI0nzw3RG4hzw8rCpOOXmDuyqktib6x3saelHFnVJt3/aUXFZpYAAZNBmuQ8HOXiYJjW4ShbGz0L0tNnrizaSlHTtE7gbYIgPD3TvpfLKso0Tc/522qSJg1kBzt9vO+65TgsBjRVZSSa4ksPnOZUXxCH2cD/d/tq6j0Wzg+EScoqKyvHJ+kz/SEOdfhpG45Q4TRjLrWx/i8fISGrfPOZDn76oWu4dsXVYfgImf+yJzJFg2ha41CmOy7AN55qZUWFHYvJgNMKkijlFHKPHQtE09A9FODXx3oYiaQYDidJqxolNiOiKLKyykk4IZOUZQTAahLZO0XzxCUWnkIbSuZDmENZTY0rtxdP+3AURRMQBI2L3slpKWd6Q8TTKmklSZcvnlNwXOYw88Zt9QRiadbWzN5Dmo94WqHKZWZbYwlpVeVI5wgCesrbmhonx7tl4uk0TquB126p5Xcn+rGZDQwEEqyodLCx3kOtx8L397WTklXuWFfNuloXgViKP5weQNP0LtT3ziCvvKbGSX8wjqxqlNlNHGgbYUOdG5vZQJndzNoaF+WO4n6I91zXMvNOs6DQdI1fHpm6yeV3nrnAl39/EYD/2tfJ0b+8Y2ybSRoXuXBbp576tjcUJjN809pKxAdPIwOb6pyczNq2OBIPVy7hhMyvj/Yu6Ht0+fMni2izCC0pssKovlHrSIzvPdfG3Ztq+Offn+M3x/twmCU+e+caPcX61ORoz7XLciXhT/fpdYQAF4fCbKr3FH4xRbKxoZjWpFcG3kiSXx3pJZxIFxxxXWLuaIAsqzxwrI9fHu5lWbmdT926MsdoKXOY+eztqznc6WN1tZNdyyY7F5OywsMnB1A1jeFwgndf23z5PkSBXDFRlokIgvARQRAOCYJwSI4tXofgezZV84lbVpHd73AwnOa+A10c7QpwsDPA/Ye76QnoefeRpMLmBjcb6z0IgoCqadR4xuWoXRlJ2ZWVTrY1lnDv1joSWXli9x+enLYhz6StvUiIgM1U2C3U7Yvp6myqXuyZfdRE35cqilgMEnaTRGOZjR2NJWyq97C2xsXn71rNTasqGAkniaVVBFHgkzcVL+O4xNw40DZ1mtBMROaQiXKyN5DztySJGERdmcueRwVmIKQviGQVLvRPzrtvKLWxsd49b/V1iqanj53pC/OHU0PEUgpWk8TKKie7lpcxFE6jAsORNFajyMpKB/5oGoMksrbWyVt2NOCNpEimVTQNOkaiaJqGySCOqUZmd+VOygq9gfhYVGcUoyRyx/pq9rSU8cvD3XztiYv8+W9OMhRO8OCJPv79yYv851OtJOXZR+w+9t8vzOk7moilwMrc5GSbdozfHO0fex2coN62sW7coN2z3DPlOQwFqgh979nWscaXz14aydl2xU6si0RaUekLT/PDLSCikOu2nO7Xtdlyf7lwQuZUb4jfHO9D0SCYUPjZ/g5uWpU/ClFiG18kqqreb88o6dkIl6uHXEouLprki6Y43OkjGFs8Y0NRNWIpmaSsEi00PLfErLAa80do4ml9DjnVG+APpwd44FjvWAuDUTY3ePjAdcvZu6Ii71xpEEXsmaJa12VQSS2GKzYnSNO07wDfAbDUrJwX1eqJctSF8JqNNbjsZr73rIlAlpTjYCTJaFxoMJTkj25cwU/2d7K9uZRqt5WHTvRjEMFlMTAUGj+uwmnGbjJgNoi8elMNLouRbQ1ujnQHMYrwz2/NLcL9x0fOcaTLz56Wcj5168oiP/nCoEJBUqBui0QsrSKJAjajxLHOQI4nVBT14snRVIb+YJq+YIxnL3hRVbhlbQV/c++GscXevzx6gVHxvWAsjWGBdd2XmBpJLr4GZuaKlKnRJizul5VaSCkaqqb3m5i0f9buonB56ug0TW+UmpTlsdQ2byRF21AkJ6KcTKdprrCzqd5NOCGjavDA8T7cVl0cpXU4wnA4QSQp86ZtdbxmUw2JlMKKjIqUpmn8/EA3I9EUJXYjJTYTzWV2Njd4SCsqz14YJhhP443qqW5pWaNrJMYfTg1wtj+E1STx/r3NVLpm9704rXOvf9pc6+B4RoDig9c1F3SMf3DqbT943w52/8PTqBq8an1uvnm280gQp/6s5gILubNvwYnRhCsvqWNxEQTon6JdwXSMFtfPBVEUyFYumm4NYDEYya42NEoCq6ocuK1GfBljYDAU58cHuvIer4j6XBRNyvzsYDfRpMwd66pYUem4bIJFdktx49uvjvQQTsic7Anyvr3L5vmqCqPcYea2tVX0BxPcuraSv1yUq3h5Y5IkjKJKKDn+YJkNAndvrOFod4B4SiWRVvjW0620eaN8/lVrcduMBGNpXmj1Umo3sb2phG8/c4mjXUHeuL0OsyTx3CUvt66t5O07GxkMJWgovTIbxU9r9AiC8Bgz1B1qmnbHdNvng/nq06MAZnE8jaKQ837u/lOYDQLe6Ljh0lJi4j3XNnG6L0RKVrlldQWv2VzLazJyr5FEiu+/0M5gKInHZuSaZj1dYjic5HRfkEhSRlE1njg7yJ3rq/nVJ67L+96qqnKky4+mwaFO31w++oIw0/d31yo3161roMcf40iXn3ZvDI/NQHcgdz+DJPK5O1fz5YfPIqsQS6u8cGlkbLJ7/Owwf/PASX5zvJ8Kh4k27/jkaVuq5VlUDBbzzDtNgToHR1B9WW562w9e7BxbhO7vmPysVDokBiL6cmdD3eXpOJ6SNT0P2mQkJeuNjkUBLk1QvNMEkTXVTt52TQM/OdDN2f4ww+EkTaU2QMNiFAknZHr9cb75TCsHO/ysr3XzuTv1NJajXQEePtWPw2zILNJctA1HWVHpoG04yokePVK+c1kJHQ4zdSVWVlc7MUp61MgoikyhMTItH7hu01y/IrzRca/yuYHClABXr4Fz5/Jvq/TYafv7u/NuOzc4Hml4cRohg18fG+Af3zrzdXzkphYeOtlHOKHwph31/PMPxrfFZj78FUUyrYI09QM/Vd3N+3Y38r19+Q2MQonNoslPhcvKRa9u9FgNAp+4eQWCIHDkL+/gG4+f57fH+9A0AfMUBsxLF3VZwYFQYqxPVKcvxpqay5dyZinSCSgrKtGkjNOyeHOqQRJ5x65G0oq24E0qX6mIgsY9W+rpD8bZ3+5HVjVuWFnBvdvqSCoqvmiKcCJNQtYVDAfDCdw2I/tavZzP9KoMxtLcf6SPlKzwzaf07WlFo9sfY/fyMpZXFC9wtNDMdHf/3WW5isvIqHFrkoSxrujTEY7FEe22TIhc318VDbR7YzjNEopR5Af7OhgMx/nuc53sWlbCtS3lDIYSY7VAb9hWzzPnhzjU6SeZVhFFON4dYF+rl32tI/zDGzblKLuNIooiN6yq4EC7jxunCKcvFlKu8ywvv78QpD8GvliaWCqNxSCRyFOYur2phJ/s7xozckTAYzXmKPH8aL+e9hdO5HoL/+MdW+byMZaYK8WsljOIcwgUTFQCrPeMe5WsedKkJMkIKBhEAXk2Sf5zQBR1L/Md66vRVI0TvQHKHGbQwGYQiMkaNpPImf4w332ugz0tZTSW2ugPxAknZOxmAy6rEYtRwmUxsKO5lP9+oY14SuFwp4+UrGIyiFwailDlstDjj7GzuZSBYII6jxWzQaTMYUIUBDQ0djSV8rZrxguV37CtjodPimjAgXYfN6+pmJWgQetwmG0tcxuX+oPjXvWzE1IWp6K5tAqYOtxzpNNHhzfKG7Y35Px79i0zXTbf7ubCYpB1HhsqAoqqUe0s3vh/JRBPK5zonjpNfapMpjfvbGIgkubhE/2XpU7KbTFiNehZBxUuM0JWatynblvNmho3z10c5pY1Vbz/BwcnHe/LNOxtKLHRXG4jFJfZfBnqeLIxCMWNbyrQNhyhxr2497IgCJgMS7LZC0UoofDQyT4UFWIpGZMkEoylKLWb+Nyda7hlzQjff74DfyxNjdsypuRW5jADYUwGkYZSK3aTREpWKHeZcZmNDIQSVDjMV7zk+bRGj6Zpz2T/LQjC64BmTdO+LgiCi7n1Fxx9j5vmeo5imMrgMYvjhhHok2NjmZUNtU4eOjWIgO7FCSdkAnGZZFohKWv8y2OXUDX4w5khNEASRVRNY32tk58f7OZsf4h4SmFllZNb1lTSOhxBjct0+2LE0jIOc/7FxsdvWsHHb5r3jz9nCrAXATjWE6TKaSKSVCixmoglFWwGkVjGwimxSpTZTexvG/fO72j2UOmy0jEU5tQ03t9ql4nrVl59kokvJ2rmEMJeVlV8mlnFhD47W5s8kCkxaSy1Ttp/aKymR6M/GGdTgcXqxWIUwWyU2NpQwu3rKnnq3DD/9+51PHfRSyKtcKovhC2tUOOx8otDPcRSCl2+GH/92nVcGAhR5jDRUuHgLdeML9xlReWZC0Oc7A2ya1nZmCd0U72bh072U+W00B9M4LYaOTsQ4t+fusjdG2t5754mFFXLTFrj3LS6klK7icfODNI6HMFhkbhlTeHPky889y4D2QvZzkBhRV5Hu6c2eA50jPCRHx5GUVWevuDlG2/fOratudTCRa9+zVsn/P7Zqc93bmgs6Dq+8Kvj+DKRqm88dSlnW81l6H17NRFJyhQjEfLE2UH2XfIuqMEz2rpUBPauLOf3Z/QeWiORyff3HeuruWP91L2pNlTrC0STQeT1WxenjYLJWFyk5vvPtxOMpznTH+KD18+vSMkSi4854+jXNAjGZIwGEVEQMBtEnFYDkaRCLKVw/cpKfFFdofBNOxrG5pmdy0qpL7HisBhwWYx8693bONsfZndG0ODcYIgNNe4cR8GVSMFPhyAI3wEcwLXA14HlwD8Ady7MpS0OyQmj6xu2N+C0mlhZZeexM4OkVagtsWIzSTSV2vDH0pQ5THgj4x5Lu0nk9rWVWEwSN6+u5PxgmGq3BV8kxfJyO73+GMvLHfT64+xYVjKlwfNyYTCcwiBCfamNKqeJZy4MkwylQABVE+gYiVHrsdDpiyMAKyucmE0Shzp8WAwCaUVDFGBilsJ33nXNonyeJcYpdxdfvaBOW1I8PWcGQrwu6+9TfeGx1wN5enWks4z0ly4OcOeG2qLfuxBG325jnYMv/voU3nCC5y4McceGGgySyLJyG4OhBPUeCy6rkYMdPtxWI4+dGeRUX4iRSIpuf5ybV1dQkVHQ2dc6gs1kYFtjCe/YNW4Mrapyct2KciJJmV5/DFnV6PLF8IaTdI7E+eLdayl35Pfe1pfYMg2ZNSocs/stB8Lz26fHYylsslxVZeXZ7vz1Iad7gsiqPlB0juQWzm9sKOGiVxc6WFObm26U7b1r84YKuo61WbLppgky1/G5tYtZIsP/vNg5SZBirkzsFZQ9rVwcGI9GRSf8huFEGqtRmrY25/17itCKnme6R+KsrZ+91R1PKQiCQCy1JCBwpXK6L0iPP841zbNvxp5SNAQBXGYJWQO31YggCKyocFDtsvDY6UGsJolQg8wHrpt8HyuqxsEOHwPBBLeu1evU6kvGo+LXNOequSUyLVsWUqb9cKefbl+MXctLqXFPdnbmYzYugRs1TVstCMJZAE3TjgmCsPhP+AIzGIxzqj/Md59rI6WCUUAvFC63c+vaKmRNZWdzKV977ALHuoMYJIG+TMrGmmon9x3uYU21k3W1LkYiKfa1egknZVZWOnnPnmbW1jj52YEuVDR2NpeyIiNv/eyFYVqHI+xeXsbay5gPPF8YRFDU8clFVuF8f5BQwka1y8JQOIWi6eHVgWCCt+1s5AcvtAECD5/sozojl2eQRMocRuLxBL6sNVZDiYVNjZ7L/bGWmEDXcPHe/nCi+JVh0wQlpDLbuOPAMkOvnYvehe+iIgCxpMLPDvTQF0ygaBAaiLCtKUGFyzrWfyoQk3nvnkaOdet1O7891o/VKCKrKrGkzIttI7x2iy5LParMZpRENG38M4qiQFOZjQPtPl6zqVaPZgVitHljmIwigVhqSqOn1G7iPXuaSaQVKp2zM3pKrXNPg8lOkzUW6KF2WT1M1QnntZuq+bcnLxFPK9w5QcjgbP+4YXxpaGrDZl+mLmMmblxVicRZFGBHo4fsWM/lSaC8eijW9+swG3SH4jx+oVOdSgUOdwbybtvfNsLvTvRRX2LjA9ct41dH8jfHffhUH2/cWViksMcfpr6IJqIzEYwEgNnXLa6vdXG6P8Tm+stT87jE7AjG0zx2ZhBF1cbqxWaDBggapBSV5nIHn7hpBZGkTF8wQSwlgwApWSUuKxzt8tMbiLNrWRkVmdRdbyRJ27DuSDrRE2BF5eS6nbbhCM9eGMZuNjAYSqBpcO/WugURNQgl0jx7QY/KxtMKby/wuZuN0eMTBKGOzJghCMJu5iG9bTGpsBkYjk1fWPD8eS8OhxE1s+DQBOgPxNl3ycuBDh/1Hitv2dFIQ6mNHn+cpKJS7jBjEEWePq//IGuqnGyodfPA8T5sJgO9gQSCoCu5/fOjF3j+4jBpRaV7Y4yP3bQCkyRyuFOX1d3fNnJVGj2SADaLgVBWh3uTUWIolGQonBxb6KRV3YP222O9SKI4ppDjH4hQbjeysc7Ni1mpb6P84xvnXkS9xNxJy8UbLoOB2e2fnYLUWJ77TESS4/dZMo/ckwG9eS7Al15zee4dUdT7Bo3WkmhAXzBBmcNCJCmTVjUiyTTxlILVKOGNpKjzWLAYDYCAx2ZibfX459y7ohy72YDHZqQ6K8ImKyqn+0LYzQYuDkX48A3LiSRlHj8zmPGyTb/kdFmMuIpoTnrL+vxNYGeDwywRzEgxZn/W6Sgtmdqj90KbH6tJwmKUODahhmQwNG6gXxqeRj5ZLOy7+PXR3rH78egEdZbFa7JwZSKJAkZJj9rPht0t5TSW2ugciRJLyfSHFjaEllS1sXFmeZZYyg/3dXC8J4DFILGqys5P9ucXVxgMzRz97BgOcfvXnkdWNTbUzr+BsaGhuDq7VdUuPHYTddM8X0ssHkZR4PxAGF80VXQzdhWIpTUuDYSp9lhYW+PiVG+QEpsJbyTBg8f7efGSl5FIisYyG4m0ypu262mapXYTNW4LQ+Eka6YYqw92+PDH0pztD+O2GbEaJXr88QUxeqxGCZfVSCiepspVuANuNt/cHwH3A42CIBwE6oD3zO4yryzy3TcmIHtYjWqwvszOcCSAgJ66duuaCg60+WgfiTAUSvLIqX4+ckMLFwYjujHjMHN+IEyvP46qwf72ET5120puWFXBmb4Qd66vYnW1ixK7iURaQRQEVE0v4DOIAhajSH2JlR5/fCzyc7VRW2JFVnQp4XMDukTvQDCJhi6Zm00srdLji7Guxjlm9AAkUmmG8qTQrCizcu2KK0vY4WpmLj24i1grj1FYEtE4xkz0EKBiQlSixzeul5VtaI9S6TYzEExiMgj0h+OsmKcmpFPRVGbHbAS32UivP4mCrgb1gb3NnO2PoGm6MZRWVO5cV02p3UzrcJS6Eitv3lFPPKVglERqsxqEmQwiO5eVomnamIgB6NHQ5RV22oajrKzSvW8b6910+WLYzQYMEvQH42Ph/xdbRzjTH2Jro4dtjbm1Ld5IkifPDVFiM3Hrmsq8AiujtI6kWF+Yc21KIolxv1nHSGF9XHqGw1NuW1PtQlE1ErJKU1nuRPvOXY38x1NtCAJ8/OapaxY+eVNh9QyrqsZTO4xXePHuYlNfYqWhzEa928TTFyf3yXIaIJzH//i3r1tPjz/O6b4gDx7r5qFTw/N+baIwLnIRispowqiaXNY8JegRVkPGcOv05dfnW1U98+Lu/qO9Y8bfhcHZjoIz882nWvncazbO+rhoUiYUl/HYrmpf9suWZGZMq3Cacc1RYS+lwQNHe9nf7sMf0UsN4mkFm8kwpjCckNWcBqRGSeRtOxvRMkJAT50boscf4/qVFTSX62PhikoHfYEEa2udOM0GNAQ21C2M094oibxzVyOB2AIZPZl0tr3Amsxx5zVNm3sl6yIyGJo8yk78Fxu62oWY2RZOqOxr9eGPpUjKGrKa4uET/bQOhZEVha0NJdy4upLW4ageThT0PhqjufgVTjP/8PtzKKrGn925mk/duoIfv9iF2ahLNY5a8G/aXk9SVrEYJc70hTjZG2B9rfuyye1OZGIedDblNgNGEfoj499ety+OUYSezPymAagqoqBrwusLPg0ts03WdMlag8BYw79IGmITPLIGUeDf3rl9fj/cK5y+ORybx74omNmKWiayAjgHLg2xOisCWpeV7lZqn5zP7rGZGAwlEYBaz8L3D+gLxNGAUpuMmKmUthgl7jvUy4f2LmM0bV5WwW0zoAGXhsIomsoz54fZ3OCm2m0Z651x14ZqKl0W0orKfYe6GQ4nuWl1JVsaPAC8bksdSVnBbNDzp1sqHPzRTS30+mP82xOtqJrKh65fzroaF/vbR9A02N/mm2T0HGz30euP0+uPs6rKQVPZ1Epm1zR55vw9ZcfkAgUWwhjFqb3pBkmfZMMJheUVudfeMRLX30+D9mkiPZemMaqy8WfJbU+MLS7pGOQSScrYJS2vwQP6IiwfqgaBWJpzA2FKbAtTG5Ct6hdKphEAVQVjlmTyh69fzk8PdLGyykFzuYNyu4lAniae2bWFU/HhPcv5/gtdxFMy16+soGMePkM2n3nV2qKOW1npIKWorMyTtrTE4uOxGdnS4KHHH2dHETU9E+kNxNEEAX80hVESqXFbMBslyhxmNtS5cduM3LS6gm5fDEFgrLmuIAj4oimOZaLb+9tHxoye7U2lrK91Y5JERFFgKJzgO8+2cWkowvUry3nT9oZ5lSK3GCWq3bMbF2bq0/OOaTavFwQBTdN+Mqt3vILIHrJEwCRBYoKT49pmK4LVyoWMPrkKdPuiVLkshOJpBEGgdTjCyd4gaUWjzRvFbTdx3cpyWofDCILATasrx8733AUvw5noxVPnh7l3ax0Wk4Sialwciox5sBVV43RfCItRT5NLySpDoeRlN3oMIrjMBhKyOmW/g5GYPCmBRlbzN5VTNbCbjVS5TJzuH1dmEwVdWcttM6BquqS4N5KetJhwWiTWLkBKwCuZuZQJO+ewupuLKunghCrj9+5t5rfHewnFZf74lslNfMvsJswGEZvZgGWKjtTzSSKtogIj0RQ2k4GUrJKSVQ60jzAUTuQ4EDr9UX59pBd/LEmnL8bZvhAPHDfx1h31dI7oHuXjPUFuX2chEEszlEmhuTAYHjN6TvYEOT8YZmujh5ZMjwSjJHKiJ0iPXz/HwXa9v8/KSicXBsOsqpq8uGkotXFuIIzNJE1Se5vIoXY/95TOLWLmsogEM9bshgLTeNumqSPTNA1REDEbNJQJqVSHsvo3PX1+mP9zV/5z/O54P5++c/2M12EySmPOILMxd+Kde9vWlxcpWeV8/9Tdi6aSrH74ZB/feOISiqqxqrQ473ZzqZUOX2F1fIoKd22oYjCU4pY14/P21sYStjaO99trqXDkTZEczkqh7A/GiSRkqlxmDJKILdM02eUwc+qvx/WffvT5oj5WDs1lVgZCSRxmA/G0irOIr0rWNCqdZtJK/nl+icVFEARuX1dNIq0Und42ikXSU3KfOj+MxSjx+q11LKtwcPfGGsKJNB0jMVZXO7k4FOGhE7r4y2u31I7NLU6LgXKHCW8kxbLy3HnEkjUWnu0P0zoUwRdNcao3yHUrKmgsW3in43TM9M3dnvW6BtgIPAmYgduAR4Gr1ujJRmWywWMAdq5tosMbwWKA0fKfpAKrqlwk0ippVcNiEAjG9YW/qglEEmnMBpF37m7iibNDBOO6/J/FKLFreSmPnx1A1WB3SynxtDJWoBzNGvkPd/rZ1zoCgN0skZLJm2u70Ms3VdWjK4lpGryNRmsKQUDvD/Ls+dxiYY9ZxJdQCcRk3Fa9J8mx7kBOHw+Aj9+4JKV5JRGcg4DXmjno+lbYcr1FZ/uCDASTpGSVZy4O85ZrcvOuEmkVs1FCEvVntW5hFasRRf3ZScsaVqOAIEBCVlHjabzhxFineYMATR47JXYj0ZSMIOjRClHQ5UWdFgOxlEJLJmpR7jCxtsZJXyDBjib9Q8iKyhPnBtE0CMRSYxMT6I1Yy+wmFE1ja8ZAuntTDbfJlZgNEk+eG+TioC6YsrnBw4Y6N41luqLbaNRoKvyxuQtCWKXx+pcSc2GjWTQ2dUSo0mVhW1MJ8ZTCpsznHSUlj5siqjr1eJYnUJiXWrd1XKhlwkKx6eorw1xQEmm1KOfK/Yd7GAjq99nWhuK8290FGjwAogoaIqFIhDJHQ9597GaJLY0l/OHMZOn0ZEK/I4bCCX5+sBtfNEU8pdBUZuMtOxqodBWvdjkdHSP6Z0ykU9iKVMvq8cc53x9i42XuK7REYciKyk8PduMNJ9nTUsZc4iUJBZLRNBoQSykc6fTzhVevxWqSsJqksfu0NauRdjSrbtYoibxjV9OMBtjKSgc1HgvRlMKyCgeVs0hDWyhm6tPz/tHXgiA8CWzTNG0w83cLunT1yxIBaK6wMRxOMhJN47IIxCLjS/sef0w3ZEwGHBYDq61GOkZirK91U+myUuUy8+2nL9IbSBKKy9y9KciO5lJaKhx87z07AL35KMDNayrxx1LsWjY+qBuyFKhuX1uFy2rEY5s8Gy+0SpAKDEXnJhlaZjMQTavIsoooCnT5YvQGc721sqZhFHVhA0XTGAwlWF7umGT0fPjGFXO6liXml8gc5E1P9BVflHxmMNdr/I3HL5LIhBYfOjHAf0yIUZc5jJzsUzBJItWXofneaJTTbNRVZhRNH1MsRhFF1RAFAUnQsBglPHYLf3nPevoCcSqdZv7fw2dJyQpWk8QHtixD0TSMGZlcQRB41YaanPcySCKVTguDoUSOwEE8JROIpahyW9A0sJnHF0Nmg0QirXA8U+x/uNPP5oyRUKiogcM0d6n9gei4sfB0a2Hl/8ZpBj2nxcj79jSTTKu4bbnXNxIbv1cvDk0ddWj3FmbJ//jF9rHX3gljZPv8l2pc3Qh6NsVsR4sNdW5O94WQRIG7Nzdy/9HZ1/TM5j0TwEMndc/2n91/IqdP1igGUaTDm79/3OhdlUyraBpEEjKKqpFWNAZDyQUzerLZ8cVHOPqVu2d93GAogarpfQiXuPIIJ2S8mSyhdm90zms/o6SvtwyCQIXTzEMn+njdlrqcqPWmOndGyhzWT8iwkURhxohTrcfK/717HcAV079nNjGyZiDbPd+GHvl5WaIBA4E4vzrSw8Z6NzeuqubnR/rHtsuZYlmjQaWpzI43ksRpMSKrGh+9bhkvtXvp8OmDSLs3wvnBEBajxIY6N6IocrY/xKEOHyurnOxePlkFaWtDCRajrkK0rOLqy7G1m0QSaRWbWWJHcxlt3ggdIzFSisYjp3M9ZDajQELWF4p2o8DaaidpRaM/kJs+UOteypS/0lhbVXyoei5T681rK3P+Pt0bmHb/Dm8MMVOs2T0Sp9S+8IsPAJvJSCKtL4gFoMZlZefyUn6yvxNFg4SsoKi6etuGOjfffbYVbzjJYCjJ1x+/SHOZvSD1xjfvqMcfS1Fu1w26S0NhvvrYRfoCcZwWA1sbPYxEU2Qn/pkNIi2VDlqHIkUpRBrmMTcbIJ0vHzYPy6s8MMPvPVMIfDrdgYbSwu4Nh3Xq6bO8oDO8ciixGmcd6bEaBD5z2ypaKhyU2Iw0XIZavEK4MBimdTr1P/Q00VvXVjIYTBKI6ymuq6ovzzz+2dcU5xiscOipbRUzpLUusTiU2E1safTQ44uxe3kZDnPxNW4GUY/4N5ZY8cdkDncF+OXhHjpHYnzuVWvG95NE9q6Y22h2pRg7o8zG6Pkd8NtMk9I08H7gyIJc1RVCJK1hExTODYS5fm8zAv1oQJVdZEdzCTazhNkgcfu6Kn64rwOTQcQbSeKNpZEkEVEATdMt4geP9dPujWE1SbRUONjXOkIonsYbGWFbY8mk4i5RFCZZ1lcDBgEsJok/u2MVz1wYpsMX49mLwwgwSa5UAlbXOOnwRpEVFYMo8NevW8+5/hA/O9hDJJW7CHris7dcvg+yREH4osVHemrmoIxqFHMH/Cq3mcjI1B56WdWIp1SMkoZjjso3s8FoEHBZDSTSKhvqXLxxez0ne0Oks4QMfnGoh4FQki5fjHhaJiXrnjWjKPD4mUGay+wzNngzZqI9o7QNRwkn0hglAaMksrbGNVb/M4ogCLx2cy2xlMzhTj9HuvyThA2mQ5ul/PBMGAos8oooU+8XScr8+KVOYimF29ZWsTGr54iR8fq16WrR3lVgg0lLVl+hifZfZfnCNeS7GkmrGtVAfhmD/MRljZFYitdvq+exM4N87dHzRb13ttT9TGwsFznpnd74LrUZSabzn7E+y3ewqd4D9QW+8Txy8/q6oo776I0tnO0PsXGRxJKWmJmbs+rDix19RcAsCVwcjNA5EsdiEImmZFKyii+TOnxxMMRPDnRR6bTw/j3NWEwGun0xBkIJNta5c+p2rjZmM/t/GvgAuky1BdjPyzi9bQwNogmZ+w73jN1kw1EVt9XENc1l3L2pmm2Npaiaxs8P9rClwU1diZW6Eivv3NXEsxeGaSy1kpA10rKKKZOmsqzcxvHuIPUlVgaCcZ6/NEKtx5IjegB6YW40pWA3SVecxZwPWdM78X7v+Q5UVaEvmJokRjCKgh5Sl1VVrwvSNJ5vHWF/q2+SwbOhxrGgnX2XKI5yV24K0WwWGLE51AM9eqqHW9eNp3ltrC+ldUSPxJryBCAGM+IBaUWj3RulZYGl4AXAaZYosZrY2+LGH0vxp3es4Q9nBqhwmHMmrGhSJpKUiaUULEaRcqeFareArGhEkzIPnujjLTvy1xdMxZYGD2f6QwyHk7xpe/1YEXY+DnX4x/qCldhMLCufWrEtm+HoHH7APMSnkvCagC86tULWUCjBiZ4gibRCjceSY/S47Sa8GQGMclfuZ9QlivXXLmthaXt7lpfxPy91A0zyukrakuxvNhaDxMZVJTx7YTZmD/zbExc51hNAVWBTkdK3s/klWuoq+fPXN3O8y4/TauSXh3u4dU0lJZlCr4dO9HO0y09TuZ2TfZNzGPP802VhNDVcAFzm4jIiDCIMhxNI4pLRczUQSSoUM4upQDStcbovjMUoomm6U9AoCXxwbzPhRJqvP3GJo11+zAYJm8nAvVvr+NWRXlRNYyiU5O5NuSnWPf4YPzvQTa3Hyhu3181YD7qYzEayWhUE4Yfoxo4MXNC0yzOyz9dS32UWCSVnp0wioGE3SYxExpNxVODFVi/D4QS/PNxDQ4mFH7x/B2/fmdvx5K9ft4FjXX7avREUDbY1luC0GPiv59vRNI03bKujocTGL4/0MBhKMBhKsKHOndM9/YHjfbQNR1lb45yUy3+lIqsQiieJy7lSrkYBEPTBeZSRaBqnSf+Fa9wWEkmVeJ46kd/9yY0Le9GvYOYS94hMUP8wGSBeoHRVcA4iQWW23BSkMsf4RJ8vz3g0nUkD3JaFH5BNBgG3zcjelRV0jEQZCid59FQ/SUXFZJCwGgWSaX2i2dFcxv72EepKrFwcjLCu1q0b+BlZ99SEtK+UrBJLyXlr/EapdFn49G2rCrpWe9aC/UxfiM6RKNe2lM04cdW55p6uk1HzBqC5pLC0mqHo1DeYIOi/tSBMnjduW1fJfYd6EIDXbskdS7PVw450+bl788wu+ucvesdeB+K5z4G3MNXrVwwem5E3bGmY0uhxm0WCeebmZy8OMxRK6vUxyeJqAE0SFFp6WOIwsaXBg6yqHGz3IQgChzp93La2CkEQuDQUwW424Ivmvxb35cmanURDqZ1uXxS31VS0c/DjPz5KMJ7it8f6+O0nr5vnK1ziSkMAjJJAJKmgarpi8I8PdNJS6cSaieSIou7QETJjqqyovNDqpWMkyl0bqlmeKb34zrNtnB8I6y0Dal2TRGSuJApe7wiCsAddqa0Xfa7yCILwNk3Tji/UxY299zydZ6LkcnaX9qmIpzWi6cnZyP3BBL5YCkWFSCLNm765n5SisqXBzX++aweyorLz/z2GLyZT7zHz04/uoaHExuFOP6F4euwcTWV2mkpt9PrjlNpNOUXEmqbR4dVLI9u9UxfeXokEspqqCECF3UC1x8qZ/smrAU3T2NZYQq3HSsdIDHmCstIX71q90Jf7imYuz1frQG7xuTILN8hKT/Hve9fm2py/h4LjCk3RPCscl8VEOBHHKAoI4vzWouQjKWsIaLx6fTUf+/FhfNEUJ3tDuK0GPnnzCjbWubkwGKHMYeb8YAiDJGI1SrxtZwNHOv3sbC6lxm2lYyTK+lo37d4o3b4Ya6tdPHiij0AsxfIKBzevrpxUsD+RkUgSRdWmLKLe1liCx2aiLxDnUIe+KDUZRPa0TJ/LfXGgj7vmmL9jEsf7L9lthU1HW2pKOdzvy7ut0mlhU72HaEqelB68uso5Zgytrp7am72iorBI13QF3/3Fa3S8LJFEAW9g6jksPIUzss5tYySSIq1oRCPFVQHWuCx0+gs79tFT/dR57AyHkwyFE5TazbxwaYTOkRhv3tHAnhVlPH5mgLbhKYQM5qb5UzSRZBo1UyOYVlQMRXjaYyl5LLNkiZcnBlEf/0wGkXU1LqxGicNdfqIpPdsmEE3z7Wdb2dFcyopKO8srHNy+rhrQVXeP9wQ50xckmpQ50hUYM3pKbCYEdKXfqoxQ0L5L3kwT7BK2Ny2wXOosmM3s/+/AGzVN26tp2rXAO4H/LOZNBUGQBEH4L0EQ9gmC8Hcz7T9fqvET62SrPNN7FgVAmuIbqnJZkDLpZqoGPYEY3kiSx88OcWEgxA9faMOX0bjuCSTpzvTbWFHhwG014rQYxpqA7VpexgevX8Y7dzXm1PYIgsB1K8spd5i4bo7FZIuFCFhNElVum57alyeDxW41c01zKUOhJP5oKie1zWZcUmxbaObUp8eRW1xcYIYSAFOsGwrikVO5LVX9ifFPMbE/C0AspS8KUopGyQxGwnzR40/SG4gTSytj0c1gXOZXh3voGIkRjMuMRJJjymySKHD/4R7d03qsj4ZSG9evrMBiFHngWB+HO/08eKKPYDxNly/GQyf6+N/9nYQTU/+CPf4Y//tSFz/e38WFwfzhB0EQaKlwsKzczmgGbSEKbr84NHslrYlkN5w92Tt9cfgo5/vyGzygR/neu6eZD12/fFJPs+8+14aS6R/29ccvTHmOx88MTbktmz/NKvhdV1WYofRKxuaa2sU41Rz/zXdv594ttVQ7jXiLNCR7CjR4AELRNCd6ghzt9lPrtnBNcwluq5HTvUHe+98H+MXBLjxW0yTH3ChzqC2fE5GkrAujpBXS6iwG4SxWVDiIJBXW5OnftcTVj9siUmIz4bIaQNNo9UZJqxrbGkpYVm6jxGrAZjbQG4gTTyk0ltrHDB7Qm5PetraSUpuJk71BTvUGuTSkT+IfuG4Zn7hlBV9+/UaqXFZUVeNAh49wQuZgx9Tj9WIwm8wWp6Zph0f/0DTtiCAIldMdMA13AT2apn1QEIRHBUGo0zStt8hzzRqDoNee9AZmzklvKrNT6TSxr208LC8Ae1aUc7Y/hICuglTpMtMfTCAJAo+c7M3p5iwCu5frctRum5EPXLds0vtMtcjY3nRlWcmzRUXXgW8djrC10YMgCgiKlmP7GEWB3xztZSSWJjHBy/THNy8ZPFcyTWXFL/aEOQRc/JHcFdDqKifPX8pEKfIsPEYyKVEa8PjZIVoqF76Rigr85EAXTrORcFYaoFHUGArr1x+Iy2yocyKKEo0lFn6wrwNN09h3yYuqaoiigCQKGA16Oly508zKKgdtwxECsTTHuwOEt9TizIwfaUXV5bAz+XyBWBpV05+2qVJyRqkvsfH2nY2kZJWG0pmVsrbOQ1fwYjg7MP12k0HM2/VbyIppTucIv9AfKOg6IkkZj8VAQlGonqAsVrUkNDmJa+rrgY5ZHXO2P0SZw0Knv/j6sdnELd6xp5EXW4PEUjJt3hjv27uc/mCC3xzzk5I1LgyGqHVb8U8R0vk/r1pb0Pt88dcnaB2K8kc3zU/fuVjGUSirIKjFRWqOdAfQNI39V9gidYlx/u3JC5zqCfHB65pnfazeBHp8Doil01waCrO1sZS/f8NGnr/gJZ5W8EZTCAI056ntNBskrm0pJ5TQ59O+QJwVlboj//qVFWP7iaIw1gR7ddXC1s/OltksOy4JgjDW/SLzulhDZS/wWOb108CWIs9TFPI0jpDsImgNqHKZuGdLHQ1ZjUFXVZh5PNOYTBR0KcEfvnc7LeU2Kp0mhiIylS4rq6rsuC0GPnzD8rGePClZ5bvPtfLtZ1pJpF5efbsF9J48RklAmpAzFUurBBNpDMLkbfG0gkEUiKWUHI+f1SDyR7cspbYtNHPx6x1s9c680xTMEGidloldoE/3jlcQp/I4YW3G8Qd702VUJwol9cbEBkEXefBYDYhS7op7WZmdbY0llDutNJfZCcVlEmmFf3/qIv5oCrNB4m3XNHLH+iruXF/F9SsruLaljCq3hRKbidHwTNdIjG893cp/Pd9GMLMoW1PtZGuj3nR0onpbPqpcloIMHoCPF1gzNN988PqKKbfFUwof+MFBXvtvz/PMhAbITZ5xS2Q6b3Z8CmWuifT4YgQSMom0xome3FoV++UJJl5V1JbM3kGiaXr680IiZP1/bXUJ5ozB7LIYqXCaef/eZexoKkUU9Bq74XCCqXrbPnRqBosc2N8+wtPnh+n2x/jm063z8yGyONw1O7GIUSRBj4QbLkP67xKz51x/iJ8f7OFUX5B/eezinM9nlAQEBJxmiYdODCCIAnUlNv7xjZv42I0tU7YxWFnlYE21k+ZyG1saPVOe/+5NNXzo+mUIgp7q9vT5IQ53Lr5BPZtIz0eB/xEE4WuZv9vQU9yKoYxx9coQMGkVIgjCR4CPAEiuqSe5+WZC2Q8dQxH+67l20sq4gdLpT1FiEzAbRGo9Vv7zndto88VxWk0E4jGeOq+nfdy1oYZ1tU5uWFk1duyDx3vH0iecFiPv2NWIpmn0BxN4bEZspssnpzsXBCZnqmnoSlRlFomBPFLGp3rDZLQMEAU9LVAErEaJSJ4UnR99aMf8X/gSk5hDlhkV1uIXJNIcUkFKJ6SoZYsX5OvB8qbttdx/pI9qt4W1U0jBa5o27wqJ/kiKUCKNrOlGj91sIJSQMUsCSUXDYZKwmY1j77+yyk7rcJhEWuV4d4CbVscosZsozfw3yvamUkYiKcqd5jHhkzZvBFnVkJMKvYE4oUQam0mapAg5X1wcirKmbnKPsWIZFTSZiZAy9Rj5yKl+zvYH0TT40Uud3Jj12U/0j6fPPXN+ZMpzBArMhjqXVZ8YmKDeEVvortFXIYdaZ58O6bJIjERTeeeb+cIgCqRVPfvgYMcIG+vdDIUTrK0Z91B/+tYVfN9q5GDHCKIg0h+Mj0Vrs6kuQMlgWZkNm8lALCXTUmD92GzYWlecZ/3mNZW0e6NXnGd+CZ1SuwmzQSSRVihzmOZUi2sQYWuDB7vZQCSlYDZJxNMKNlnNaU6aD6MkctfGyaJavmiKXx/tRQBev7WOEruJw51+jnYF6BiJ4rYaKbGZKPv/2XvvwMiu8v77c9v0pt7LFu1qe7G2et27TTFgY5rBEEpCCikkGAIJLeD8wptAQiAhgRCKKQZDwItt3O31uu16e6/qXZpeb3n/uCNpRhp17Uq7ns8/KredOXPuOec553m+j9OacxfpYjGdGbYEvAwMmWoJ4EvAe8a9YnxCMKy25wHGmK2GYXwH+A6AtaLhog0hox/UG1UhqmJk+MmKokBtkQNdt/Hx65ayrNyD3SrjtJo+kfWFDh4/0o0kmhlpT3VHKXFbeOv6qixltqL0JOb5U3283jyI0yrx/m31l4QG+nhfSFwjp8EzxJCxY1dEDN3AQCCUUAmMmjT8vztX0FR/8YzdPDMjpc98Sbt3FvKug9HsNvberXU8dcLcdVqXw6g50xvDYTHz5fQEE1nKZ/GUxkN72/BHkty2pnzMtTPFIgtsW1LIL9JJjTVAEgT6wkkMw0ASwKaIaOmFlmhSw2WRAdM9TRAElpbm3pFYXu5mWZkry0hbU+WldTCGXZHwxxI8fqQLSRR4z5barH5nrijzzO09rcrUVpgdE6z8Lyt3k1QNkppOpS97Auq1mQM8MBxsm4sblk5t0ieI45dj4NLSnbko+KPTD8r5xE/30xuO45TBECByAYQCMmNgVld6eXhfB62DUQqdVqJJlZ+80sLRziBVPhub64tZXOwkmEjxj4+NzRv0/q11Y/43mlKPnR9/eDNneiJsW1rM1/549p+hqc7LnuYAtYVWPO6Z7d3ftqaCw22BCeXt88wfpR4bX79nA0faA9yxroLvfnLm93JbZZZXeLDKIrph4LVbONUdQhYE+sMJimYwXpzuCQ+LdJ3pDdPkLMSdzolnk0UskogoCDjmK/AtzXSMnt8CjwE/Y3LRs8nYC9yAaURtB/5jlve7ILgsIpoBFlEgqRmoqo4swj0bq1hbW4hdkbiu0dzFqSlw8G/v3sCxjiDPnOjhaGcQSRQYjCQZjCZIaTqtA1GuWV6Ky6ag6TqbF5mrpL0h0185ktDoCyco89iGg5sXKkNTLUmY2F0wE1EAj00kmtIp99gIxFKIgkAglj0Y3tZYzDu3Ti1BYJ75xWqfeQcWn6ZCSWYOoI2Lswfm506OuNmdG4wxmkgixWA0hVUWcY/qdHtDCfrS7+DxrrnRGm4sdZLS4VhH9v0C8RSabpDSzYWDQFxF13Xa/TEe3tuGgcCNjWXIksA7rqiecOd39K5UkcvKvelJ19PHTfdbTTdz/VwIo+dkT5jNS8omP3GKjOcyNJqJVN48NoV3XlFNXNXYsjhb/KXIbaE9aH7PlQXju/C1Baa21ZMZFzb6NZgr8Z3LiaZ637SvOd8fGRYgWlQgcW5w+vEqiwstnB2YmsF1pi9Mmz+GYZjS5T99tZXfH+0mEEuRVHXW1fhYV+vl4b25Pfs//9sj/PpPrp70OaUeO6WeWWRnHsWxzjCyCF2BJIORBIWu6WtnX7e8NCv5ZZ6Fx5pqb1busZkgYo4d66q9tPvjGAaUuq2omo4gCAxGUzMyepaUODnY5gcYVnW7oq6QQqcVp0Uy801apawk2tMlEEvRF05QX+QcjludLtMxemTDMGZhW2bxS+DHgiC8APzcMIz5d/QbhQjIkoCgC5T7bKi6Tkt/FEUUWFvt5W0bx0q1+mMpXDaZT922goYyF7/e30GRw4JdkfA5FCp9Zic3Wpjgq48coT+90xGJxCgvcvPeLXULOhmngWnE1BXbOdsbm9T1QMCs08GYjoAp121TTNeFTLxWkW/ft+XCFDpPTmYTfmAXZr4JO93pS+b5bQMRNtaNTGp9GbNOOccKfLs/jqYbxFMa5weiVGRMess8VhIpja5QnJtWzs2g3x9JsqTMjarqKKIZYOyQYUmJi75wgsiAaZjpupm0uNMfQ9UNJFHgjnUVrK7y4sqRb2giTnWHCMRSrKvxsXVxEbpuJtqszYjRiabjCOfEjTY1t9sZ4hQHsYnmr+d6Anz7+bMAPHeyl1tXj+zcncgwaF8+M757W3P/1LYTejMkq1Uje5FqZenCXrS62IQTKl/57aFpX6dlWI9dgekbPBYRllcUcHage0rnD4TjFLusDEQSNJS6cKVXqr12hTevrcQfT/HQnlYefKUl5/WnxlFIvNBYJNGMjZUE3NaZ9egJVaMnaC665hICyXN5oAOGofHY4S6Wl7vZUFvA+b4I+1r8LCt3YRg6D7/eRn2xGWs6VYpcVj581djF6qkmu56MeErjwVdaiKc0VlV6uHnVzLwypjPyfUUQhG8DD2O6tgFgGMbz032oYRgJ4K7pXncx0YFAzAywFwXoDsRI6eZW+N/86jA3rq5EEgVssjlxHwgn+NLOo6Q0g3c2VbOjoYRf7G2nIxDH51T45JWN4z6rP8O164mTfdy5wU5fODHlgOL5QjdMt6HJsAqwqMzF+b4Iqmr6TiuSSCSWPbkQgV3333hhCptnXGbjMdIenvnVy2bhRdEyKm+VnLEzKueYPMdTZh4C3TDjzjLpCSWwKhJ1hU5aBiZvz1NB03VC8RRXNZTgtkmc7o2wrMzDh3csYvfZfr73whkiKQOrLGK1yKys9NDuN5+9rto37QWPfS2D/MPOoyiSyF1XVPOOK2q4cWX2LszQbhLAO66oHl6EmSlznjpsgu2RTIcdmzG+ktdn/+/o8O9nRxUw0z6XpfGN9YqCqUmvZebpSWrZhRenum31BiGW1OgMTd9o8VkFBhPmdxWbQZUmdUipU3dM6Qwk+ZNrl7CnZYC7m2pYWuKiyGnBZZXZsriIf33qFGd6QmNif4coH5ULK57SeOFUH4okcFVDyYxXpydjx9JCXjo3yNISJ8IMn/HLve10B+NUFdh5Z1PNHJcwz1wzm5Y0GNN55kQvu071YVVaqC6wIwA7D3bx+6PdlHuslLltNN69bsHEmSdUnYRq9iGh+MydzabzabYBdwJLGFl0NYBpGz2XCuYkyeB8f5R4amSQTGnw2V8dIqUbLC9zoxtmcrBUOj9Iy0AUPe0nbBjmDtGB1kHW1eSe5dkVkVi6F71lRQkrKjxUzXJCMh9I6cR/XqcFWRAosEv0RVIUOCzUFjpIagYt/VFcFhGdsTldnvubq3Db87JHlxKVs4jr6JnFpNljz+66htzTAEKJsZOrFRVuDrUHsFukMYG6HruCRRZJqjol7rlxA/PHNdSBGIda/QzGkgSjKZr7w/zy9VZCcVOlUAA0w8xy7bOZMX+H2gL85kA762p8NJZPTVZb1w3+96VznO2NkNIMOgOn0XSDu5tqslzgOvwx/GlX0s5AbNZGz9uaxkrvT5chQQeYOAg8U2yjPTj+7PcjV9fzuf87DkDBqDaSqeoXjGW3EZERm+tLb107abkBVmaoG9mV7ClI+PIS5pw1BuB0WpmubEpDhY9X0wlz7cBMliSePNE/ZSGE1VUejnaFcFgUXjk3yLIyDzesGFk8uGZZCaFoglfO+XPmw9lYny3ssa/Fz+F2M4Fzscs6JnfUXNEbSWGRBfzRlJmcdAbu8QORRPpnPrPupcBsA9013UDVDVKaTtuggdsmYxg6g2GN3mCCM5YIp7pCrFsgMV5eu8LNK8tp98domkUal+kYPW8FlhiGcdmGaMoCOK0isaSOJImIgNMmY5ElAtHk8GRKAPY0D1ListITTHDl0mJiSQ2HIiKJIsvLPSQ1nauXlfDbAx1EE3E+/9uj/NVNy9jRMDY4/+Dnb2H/2W4qi31UFVx6xs4QQy+hLIAiC5zui2EYBoG4SjCRIhBVqSu0U1Xg4PlT2VLHW+q81BRe+NwpeeaW0bsm00GdxdjqtWcbJ7GM/E5ajsnI1cuKOdEVpNJro2LUZN9jU3j/tjoiCW1K6ktTQTdM6ePeYIyTfeZULdAb5Vx/FImRCbiq6ejpXQHDMHj6eA+heIrHDndx9bIS3nFF9aSJQnXdoC+UAsFcWY4mVX78Sgs3rCjL8s22KRJt6Xgn+xyIpaja7HczKrxWzg+YOyarKqcmIHBFrZufHsztnnZVQzkfujLOQCDEjWtrs44Vuyz0pPM7NZRkB3tnfpIfvtzM1mWTxyqVuCzDxlKRy5qlxmNcmAX9SxarLFLlnX7yojXVXg60DJp17BRpC82szWX2CC4Fcm1QWyWB61eU88rZfva3+nn3lpH2E4yn2Nfip8pn42/fvJoyn4Mv7zw25h41o8QzhhQXRUGgwHnhkjc190foCiQIWtUZCyHVFjrYdbqP6xvzcT1vBATB3P0WBNPFe2Wlh6MdIXQjSThh5nc71RuektGTUDX80RSlbuucK6BmsrLSw8rK2c0Tp7Mc8Gvgss4UqRoQiuskdRAw2LK4iLdtqOZrd62hptCcKIkCuG0SXrsFWRIpdVtRJBGXVWZtTQGyJHKkPcDXnzjJ682DYOj0RpL0BOP86vV2unIEyQZjKb77SjuffOgAj09B53+hohvmyrUsimypL8QuCwiCuVvWH0oSV3XO9EXHGDwi8JOPbZ+fQueZFb5ZBMj3z2KpqnTU5ELKWNnMFRvy41daiCR1TvWEefns2Amz26bMmcED5sKIxyZTMCroXtOzZfEVScCRjt0RBAGnVTKTHgtmYtHzfREmQxQFNtb62FRfSLnXhkWWKHJahw0bXTd45Vw/zxzvobrAQWO5h6Q2e0HMeGL2K8JtGfrQu89NLb+IrIzf5ip8NhrK3CyrLmb1KBW/zDxFW5eML7XdPkUhg7N90eHJdDCevXPUPLNUKZct8ZTGT/d0Tvu63+xrJ5F+ZzpnaPCM7g3G88hNaAZne0P85kAHJ7rDPPhK6/Cxp4/18HrzII8c7CQQS+WMXQD47YGOrL+Xl7t575Za3re19oJ6b3QHEhhAOKExEJrZuvS5vigVXjtneifvc/Jc+thlqCuyY5UlWgdiPHa4m+b+CAammltdkZOaCQRfhlA1nQdfaeHBV1p4+njPpOfPN9PZ6bkZ+IggCO2YaV0FwDAMY34y1F0ghrrVaMrgQOsAa6p9/Peu85ztMbflJeCG5SW8f/tifnOwA5dFQjcM6oucvHZ+AJdNIqHqdAbinO4J0pfOBH/l4kKqCx0E46msyVU4ofL1J09yqC1AgcPCi2f6uGX13MnmzgftgTivnBug0GWjKxDDqojEVX1cmbeHP75tOHlrnkuLYx2zyfIzcw40B9i2ZGRFcnm5BxFzRXe0Xz2AqmqouoFugGtUvIymGzx2uIuBSCLLlWU2yCJ47TKdoez4E4sk4LPJDMRSGAZIokg0qYEg8MzxHg60+ilyWYgkNAqdlikFgYqiwH1XLqJlIIpTkTjXF2FZuRuHVaY7GOM7z53luZO9eO0KNYUOrl1ewupZrpYBHOjws6q2ePITJyp7xox0qh45sfj4MT1WWeLdm2tzHnvh1EiemF/t7+Czb1498mxGfLY/cWPDlMpRYFeGjZ7EqETTee+2bHpCCcZm9piccIar6vQjgkxsskBsihKjL5zoJakZiALD8rtgSssD7G0e5PmTvdyeI08JwGBs7EJAaY7+aK4R0v57Zg68mcVgLCpxcqYnzOJ5zKGSZ2JeOtNP22CU7Utn1+8CpHSBaFLHbpGIpjSSKR1RABGBxSUubltTMaVdlbiq408nw86Mc1yoTOftuOGClWKe8NoEAvHxO8P+qMbzp3po7osytJCXMuBMb4TzAxFaB6JYZQmXTebZEz20DkRZW+nmLesqUWSBl8+NiNI1D0b5+PUNLB3lVtEfTuCxKcPb4LfOUJFiodHmj6OIAh67jEUSscoGveGxA8Lf3tzA+trCeSgh1N+/c0bXnX/gjjkuycyYSfnnuuzx2MwD/32zeG5s1C7DhlofDotIUtPZsmhse4qndW91g+HYuyE6/DF+vqeFQDQ1u+jQDHQDugKJMYIEpW4bOxqKeOJoN4PRFAbmhOpIR4DjXSGOdYawyAIrKjx8YHv9lJ+Xmby0odx0E3vqWDe/P9rN8Y4gqm4QSWrUFzm5KoeL7UwQ5yBd5N1XVPPjV01xhb+7fdWUrpnpSrQiicTSgbCOUTmBhIygnpPdEa5bMfn9njw2oggWn6puf55p8enbl/PFR44Pu6zOpJYXFzsQRIHDU1igOdYVojuQIKUb2BWRgUiSRw93Iosi1zeW8ou95u7Pj15pznn9hZCGnwpWSSSqmpNWn2tmbnRvXlthygovYNXYNzL+aHLYS2HXqekn+h1NUjModlmoLnTQE4gTV3WSms6KCg8furKejXVTm5e5rDLXLC/hfF+ELYvnLln1hWLKRo9hGLnf8kuYiQyeITrSyf4sYmrYD/9gR4jm/iiN5R50w6DIZeGh11rRgadO9vOlt61hbbWP7z1/lkT6mvVVXn72Wiu/3tfOp25bTnFaR7+mwMH62gLqip1c31g6b53mhUAWocBhoTsYI5TIdk0QgNP/cCuSlO9gJ2KmhtnFonCCnCmT4ZzFgqI0SlL1RFeQhKqjG3CyO9fkRkDAXBHtCmYbaqe6gxxoDaAZBk8dnZq07WQIgqnoZdFHyikBVy0r5vrGUp442oMkgCIKGAaUuGwkVQ1RhGK3lfri6Ss39ocTPHq4C4ss4rUrPH6kC7dVxm6VqClysLHWx11zqMoUTcw+pueXr7cN//6Pjx/j+iks+lhmmBuqwKEQTO8cjHZltCvScMzmVGVa795UxaNHTXeOQodCZuaWxfnF8iymK78+xPu3L+b92xdz1T8+TWuO/FtT4Xh3ZMrGUrFLpnnQXK0+3RvlcHuAnnRup1WVHqoKHLQPRvGNI7hTWzCzxKCzJZpe1NEMONHlp7Fi+oHe8ZROVyBGdYFjXhOkG4aZXNgq5+cGmTgsMl67QiCWotJnR5mlEqAIFLos/Oe9TcP/O9Ud4nRPmBKPldaBKCUuC/3RFF67MuE7vLG2YFry1vPJwtCiW8CE4iob6wqIxuIkMxaXY0kNBHjTmgpK3Da+9MhIUONr5wbwOK28f/sifrG3hauWlbCq0jfs7/jciV7ecYU5+RBFgZtWzl2CvwWFYXA6x6rsW9eW8o33bJqHAl14FsLuy8WkonBqwee5SIzvpTQp53uzDZuuQAI1nfCzM0dMxo6lhTx+tBePTeaWldmuKU6rjMsmoesGhXO06KDpppqhJAjDwe4WRUQADrUFCMaSpHSIJVVUXae2yEFtkZP11T5CCZWrG6YfTHyoPWAmWg0nMAxzIm8Af3Z9A5sWFc55wmOvffb3y1QebZ5AAzszGqK3f2YuleEM0Y3BaLYDWqYQxvn+KJumsGIZSWjDqmCj48jEvAhlFtUFdq5cXcrvDs/M5z+anLnDoEUWiKWmZvbsWFrC3ta0K7to5hg52OZHkUSqC+z87wc38/yJHva3+9nfGhhz/ebF8+O1kIlzhgbmVx89xpmeMCsqPXz2jpVzXKqpkdJ0fr6nlZ5ggmuWz82O9OWCRRZ579ZaQnEz2bQizc7ocVol6opGVmdSms7vDnWhGwZPHO2mptCBP5bEa1OwW2Tev61uxm1rIXHpf4ILTELVTYlbXWPXWf/w//e3+lle5saqSJR7bcjCSMjKwVY/uijwwsl+HFaFhjI3TfWF7DrdhyQKrKvx8XrLIElVp6muYEbykvONVTSDSyda643lcMJ++i+uZHGZ70IV66Iw17svC303ZyKWzWBFcQh9FoEPtaN2QjKnNLmGgpM9URRJJKUZHOwM0JSxdb95URF/dkMDzf1RPnTlIv79EzMv1xAi4HMoVBfYCSVSpMw1EpKaTiCWYkhgTjUgmV6lLXFZMTDdGF5vHkDTDTbU+sZVw0mqZhK5vnCCW1eXs7jYxaG2AEVOC5puJle+ZVX5rNVuxmNF5dxO8Caal2au8dtnGDETyDB0ugPZBlamd9pPXj7D3Zsm3xELxDUkUcAwDKRRE5B8mp5s+sJJzvZNf6emL5zg8SNd1BQ4GIwEJhxvxsMiisQmiAiSxJEkqIfSGeUBbLJETaGDN62txKaI+Bym29iVy0o41Zvb8P7nx4/TFUzgtStsX1o8JhH5xaDKN7Pdpj3nB+gNxWeVA2W2BGKp4Z210zl37N/YWGUJq8vcAUuoM+tkLCIUuSx8aMdi7s7Y+ZcEAbdNpjMQI5JIcaYnRCSpsbrSSzylEUmol4XRc+nNti8yMjAYS/GVd6wb/p8EHGj184u9LTx2sB1NN7hvxyKWlTnx2mUePdrDE0e6CcVNK7mlP8YVdQX8171X8J17m9B0c7fnpTP9vN7in6+PNitSejp4chr8x3vXXvIGT55scu2qTBXXLHLvJkZlB1xV4cauiFhlgWXlY3efApEECVUnmtRQRjVcQRB4Z1Mtf31LY5bE83g0lkyuwqRIAppmcN3yUqyyueOSVHVsssRtayqwyuauj1UWsSnmQHJ1QxFne8N0BmL869On+ecnTrB3HBkwTTd46Uwfp3vCpDSDIx1BaoscfOyaJXzixmV87NrF3Lut7oIZPADdwdmrt2V+E4uKpqZuVVEyM79xS0asgn2ChHu58jzl4n1b66n22bAqIn94TbaaV2I2GX8vQ8Jxlea+0LSvO9jmpzsQp6HUxeoZ2g+BSb7P0nRuLlGA+qIRgyGS1Hj2RA+ffvgQf/2LgxzvDPLy2X5++moLq0YpAw6hY+64xlVtOD/PxeZY+8DkJ+WgJ5QgFNfonsdg9CKnhVWVHnwOhab6S8Ndar6Y6T5PUofBaIoXz/Tz0J422gbNBSBRFKj0WTnWGWTX6X5+d6iLvc0DlHlt7GgoviiCHBeDS99su8DUl7mo8NoIJTQ21flIplR6win8sSTRlME3nzlLfyTFOzfVomo6L57qI5RQSaoGBU4FBLh2eQnffvYMdkXkbRursWbEI9iUS9Pu1AEMc6DIkRYli0K7yOt/f9vFKFaei0yBcxZdyCxWw5eOUhiqK3YhiwKplJ5zoj8YM1cvDeBsb3bCtZSm88jBDvrDSW6ZQkzJ8d7JV6zjmoGGSnsghphO2mtVRNZW+wjGTB9pVU9S4FCG3c4sikyx20pfJIkomIILfeERH8ADrX4Sqs7GWh/Pn+rl9WY/rYNmbOGadNJDS7pvmSy3z1zw230tXLdyJppcI2R2HVPN+aRO4utvGKZKnzTK5ext68r48WumpPAfXrsk61hm8srPvWXNlMrx69dbOT9gtoWvP3k661hg5voelyW6YTCTDQS7IvN6i5/uQAyP0wWDc7/6L+qmUSQCy8uyDe+jnUF0wyCpGpzoCnG8O8RgJMmRjtwGTU2hnXU1PhwWmbXVFyYR6WQUzbBPLnZasUjisCDKTEiqOt2h+JSkjnMhCAI3XyZiTheamWYdEDDfx65AnK5gnINtAarT39eh9iCne8KmoiigaQbVBXY21Zu7+ppujOlXLzXe0EaPzPjSoookUFtgx22VUSSRGq+NY50hoimNxcUO/DFzmNQMgz0tg7jsFpwWGZddNhOaSqZMs1U0CCdU4imNA62DHOsMceuact6xsZqkprO0dH4CH+eKiQweqwQ/+8NtrK+Zfz/nPBeGzv6Zz+5aZrGgeKY/2z3p28+cHg5S/85zZ/jLm5ZnHc8cIPrC2Q/uCsQ5n44nOZDh3jJbNA28NhlVH1qVE7iusZRALIndIuNI6RS7bFlSzZ9/8yqeONrFcyd78TkUGspc/OCl84TiKrGkiiSKGIZBPKUjiQKN5R7u3VY3LwIoB8/1TX7SNOgMTW17xCeN3+nEUxo/e62VQCzF7WvKWVo6suvX4k8ii6agxZH2YNZ1mXfc39zP1csmj6l69MhITpbBaHbZ5zDl02WBIgkUuSz05VDwnIiBaAJZgkBcpWNgFkGAE9AeMmcBqgH//vTZrGNvW19Fx2CM7mCcX+5ro8Mfw2WVUccZ+DoDMT5z+xSk/y4gvTGVmZgNH71mET95uZUPXV0/o+eqqs77v/sKbf4YO5YW88A71s7oPnmmxkz1Ig3AaZERBHjpTB9JVeOWlWV0BuPUFjiwyiIeu0xS1dmypGhY7fP5k73sbR5kWZmbO9bObrFrPnlDGz12q0AokbvpqJpBQtURBIFIQuULjxwlkrZ+u4MJ7rqimt/s7yClG6ys9DIQSaDqBjZZ4oq6AjoDER5+vQsDaBk4gUUR6Q4mWFLsoMRt5brll3fW4/tvWsIf3tA438XIc4FRjZlv18xmvWi0b/HJnpFJbC754EK7xEA6yGx9TbZ7VInbSpHLgj+aMuP35gibIlLgtGKVRVRdx6aIBGMpekNJ1td4efX8AIVOhVhKR5ElArEURzqCyJJIscvG+f4I//bUaWoLHQRiKTTdoMhlRZFFrl1egtsmU+K2zpvi4+bl8yPAsr95/CSXXYE4AxFzYn2yO5xl9NQU2Hk1bfTUFY2/En12nHiN0SwtcvM4uQ2/ma+VX564rDKyJFDltdIemLrx8uvX23j2hFnHFQ6IzCzv5pSJG9l9R4XPzpfftoa//Nl+2rtCRBIqG2t9DERSHB5lOAPMQb7e2TPDWI9vPHmaYDzF1x4/yW2rK6d9fW84Qevg3C8e5Zl7bBYJURBwWiSOdYb4xlMnGYymKHBYWFvtoyeU4J1N1cOCWwDHu8z2frI7xK16+SW74/OGNnq0HAbPkJuDAXQE4tgUCdXQSKgaggCGAbWFDuqKnNyzuZZ4SqPIqRBN6rQMhBEFM86hbSA+bIl3h5NIQlpZKpiguuDCZWaeb3Ys8vKjj+2Y72LkuUgUu2c+vZvNxLDCmz3Rr/PZh/Nw5OrUSj12BmJhFFFgUUm2a5xNkXj7hioGo0lqCudGa9gigSwKHOsM0lDq4kiHKTDwb0+fpK7IxdPHe4kmVXpDCf7r+bO8c1MNr7cMcqo7TOtAlL6IOTGURAFVN6gpdLBtcRGyJLKszIUgCHOWb2emvGVd1Zzeb6qOvue7xveTqiqwE46n6PDHuGNt9nr3qgo3P9fMbbfRrkeZQjTXLp/aOrk/IxHl6OE/792WTVzVqfE5ONE9vbieJ46N5CPpvMAGD8DaSi9PnjRjYjLndE6rRDiewmNTuG55GY8fyW14z6Y/nCtKZrjN6I8m0/nMZma5lXttbFtcxKGOIG9dP/O+IRBN0RtOTCkxc56Z4bMrrK7wcLY/TOtAjF8GYlgkkY11BdgtElsXFxFNx822DkR58XQfsiTitEJjueeSNXhgHo0eQRBuAj5vGMaV81WGXH1ophmkG+Z29TNHe1lSbBt25TrSGeLBK2r416dP4rTInOyOsLTURYc/gSIJnO0N0z0YyHnfxnI3d26Y28nCfFNoE9n5Z1dSUXjhgqbzLEyePdHF+66amTvHbDSCosns4GS3faQryyWwcbbPlE5P6QaPH+nivVvrh4/1hRJ86uGDROIq75yCatdUSGqQ1DRePNlDIKGjGXCqJ0JPKEFTvUY4kRpekD3TG2LX6T7KPTYOt/lpD8RwWmSWlLi4YUUZNzSW8PzJPp463o3PbuGFU72srPBkZeVu98fYc36Aox1BIgmVezbVsKbaN6vPkNJ0jneGKHJZqPSNXag50Opn67LZuTkoaRVIgIaSqcUBRCbYiDnRFeLZEz1EkhqP7G/nL28Z6ZP+Z3fzcCziN589y7WNI8aNVYR03lJKpzhpLPWO1IlFzm508z/1XVgkUhp7xhHlWEi8dn5EBCDzG920qAiXVcYqS8STGr871JXzeqsx/1s9sRnu9CwtcdLmj7Noiu/haARB4GvvXI9hGOMqTk5GLKnx41ebSaR0VlfNT0zUQqZ1IEqHPzbruil0WPiLm5ezt2WQbz99moFoAlEQaChzU+G10ToQZW36GbvP9A0LFt23vZ6CWcR8LQTmxegRBGE78Day0y8sSCRRJKbqPHUyu8P2OhQqvHZCcZWVlR62LCqkbTDKwVY/bf4YsVHu6dUFdq5eWszn37r6kpSozoUEvPSZayn15Fdk3qic6Zr58uvUNLJy0zoqpufFMyOTlVzSx26bTH8khQCsH7XKf64vQiCaQjeMcQOUZ8pAfGQCYmAO6kc6AlkeKK+e7edYV4hrGorpCMbpDSboIUGF10ZjuZuEZvDS2X6OdgTpCMQodlnZfaaPxnIPBgb9kSSPH+nidE+YY51BChwW/nf3eRxWCVXVqS50cHdTDSXuySfzmm6gGwaKJPLsiV4OtweQRIEPbKvH68gWR1hTO/vs25kifKd7p9aWTkzQcHaf7uVsfxTDgF+83sFf3jJikG+qL+RMOm/YjSuyd8kiGff8/x49yvY/vWbScjgy9BTUUS6Vs4lXuxxJaPqMYxAuJoEMm6XIOdLeb1pRRrnHRoXXxrHOwLixrOcXgMpysWNm06p3NNVwqifMqlkqPs7U4AFTCGFImTMUz0sgZhJOqPxqn6kW3DVLhb3qIjtPHuuifSBGgVPBYZW4rrGU+7bXj0lMW1PgoMMfp9BpwWW79J3D5uUTGIaxG9gtCMLL8/H8iXBZBMJJs0dTBFhS6qBtIPdg/JZ1lfzf/g7KvVbW1fhYXOzi7d9+EUUSsoye2kI7D398O8WuyyO69Qf3reXqxrnL7J7n0qXcMz/b3M19o2YX+sQmVInbSjieQpEl9FHOSHWFdgaiSUIxlZtXXdg4lYRm0DkqpsEfU4mmdB470o3HJpNKz6iiKQ1VN3BZZFxWmYFIkkhCYyASwq5I/PDl80iiwGA0ybHOEIVOBbsiYVMkjnYG6Qsn6AslsSkSL57u58cf2Tph2fzRJD97rZWUpvPW9VVo6WQzumEKtoxmrpduZmMED5FQdSTAEMbuviwudbKkxIEkChQ7x++L97ZPbeb6yjn/8O9zUfbLGWFWEXzzQ+bipN0isXmRKcijasbkkqXzSGcgjtsx/dX4dzbV0B9JzipG8Ku/O8qRjiB3rK3k3Ztrp32916FQ6bNzsjvE6qq850gmogDxpEYgnqJ+Fq5/EvB/+zr43YFOStw2rmssZdOiQm5dnXvXfvvSYlZVenFYpTlPcD0fLFizTRCEjwIfBZA8F893fcjgAXPgbB2Ijyul2hGIE06ovHAqxLMnellV6WFdtY89zQPIokh9kYOvvmMNy8svj23aI5+7Fqczv6uTZwRjprqZs6QrkB0bEJ9E7thpkdANEBHw2rN3LM4PRClyWih0KPSELoxC1ESUuhSiqkEsqeG0SDgsEppuEEvqeG2myo6A6W7mssqAgcMi0+GPUe6109IfxWmRsCky9cVODrT6SWk6qmZgYBotU8m90TYYI5rUCMZTPLSnldvXVuBzWCh2WXPK2O463cPWhoUlyPKnNyzjtfODdAZiPPD2bPWok52h4d2k9sHZB4gUuOdHQOJSxHIJTpY6Agk03eBgmx/NMEikdCq8Nh56vXlWrrkXGotlZn2yLIkUOCwzjtcYCCd55ewAcVXjqWPdMzJ6BiLJYYW8A63zk+dooSKmBwJV05FmsYagY4r9xDEQIgkSqkFj+cQGZuYu/6G2AIc7Aqyp8g672f3uUCdPHetmQ20BxS4LwbjKDY2lU8p7d7G54EaPIAjPZvypGYZxw1SuMwzjO8B3AKwVDfMys1J1TJlYITOLg8mmLz/BQCSJZoBDEVhaagZ33bmhkvdurWVxifOy2dk59aWbUZQLn/cjz6XHy93z89y+aPa0o3cSW+VcX5iUDqmESl84Tm3RiPHeUOrCMMwdlxVlF391cfsSH8+fCeCPpmgdiCGKZuzRofZBPvrDvXxkRz2/3t/OQCRFgUPhmmUl6IbBnRuqePnsALGURk2Bg2BcZffpPiJJFZdVYlm5m0hCI6UZ3Lu1DjDdR050hShxWykfFbuypMTF0YIgJ4+HUAodPHm0m49fuxRxnEnQtqULM3ngD/5gS87/P3eyZ/j3nYc6+MQoWfPpEhvtw5xnXPQcO4WXAq+3DLLrVB8nuoKUum0UuaysKPUwdkawcDjREaS+aPoLrY8d7uRYZ4g1VV5uXDn9HW+HRUQSBSIJLb04M32cVglV0+kOxlmT3+nJIqXpWGSRcq+d1AwXG0f35LWFTj60o35aO0fPnuhB1Q0GIklWV3kJxVP8Zn8Hg9EkbYMxVlZ68NgU9jQPTinv3cXmghs9hmFce6GfcSGJJHNb1X3h5HCnF08ZeNNJBpeWuqkpnEWq+QXE6S/fgiwv2M3APAuA6+vnx23FrkzvuQPREQekV88OsLFuJB4lnNC4Mi0K4JjhYD0bfr2/F42MSVQ6ziWuG5zvi/CDl5rpDiYwDAObYqU3lKAzmKD2TD+CIHBFXQEFTgu6Dofa/YQTKRKaQSCa4oYV5dyzqYaaQgeH2vw8drgLzTCIJDSuWVaMyybTOhCjqb6QRcVO3tlkuq22D5qrraIoEE9pxJLamADWXScHuLLh0hFlyYyjSszBDmXLYGTW93ijoC1gd7CJGLL3pXSGYUmEG1ZW8MDbDT718KH5Ldw4XNc4fblpgOOdIRIpjRPdwRkZPZIkcuPKcuIpjdoZzoGSqk48paHpBqFE3mk0E7dN4fY1FbQPxthYO/0FJ5cFyt12BmMqmmGwqsrD+7fWs7hkerkiawodnOuLDM9zHRaZ2iIHg9Ek1QV2Cp0WNN1YsCrF+RntFBAFYYxPuygKwx15kcvK/bc2srzcfcmLFPzmD7eytn72Acp53hj0hudnMlPkyl6ZWlnq4GjP1FyWBqLZgsJVPjsNZS66g3E2Lbr4uxcq5uRKJDuJqoE5WewKJRAFU1o7pRvsa/UjCQK7z/Tzjiuq6Q8nuHJJMcUuKwlV44kjXfSGkwRiKkc7A3SHStjf6ufHLzcTS6moOgxGEhxq91PisrKy0ks40cOi4kUAvHV9JW2DMSq9dqJJlR+93EwkoXH1smw34yrvzHM0XShUTed3h7vwR5PctLKMigyFtZQ2sjsozsEkfEdDKQc7z836Pm8EcsWELUQUwOey4LLKLCp2sqGmAKssccuqcnTDoNRjxWmVuWdz7aRGTyCaIppSs9rghcIpmWIcAqAaxozUA1Xd4EhnkKb6mfWBiiRyz6YaOvyxGSddjyU1jneFUHWDo3MsKnOpYxgGZ3sjtA1GZ2RQhJNwuj+GJIBFFnnt3AAvnRmg1G3loT/cSk3h1L6zt6yrJJiWbwdzQeBvbllORyBGuceObhgkNX34+EJjXo0ewzAmjqxdAHisAtuWFPPS2QGCcXPloc5n4f47VuGxijhtVlZVuC5p96+3LLPwrx+6ab6LkecS5FDu3IwXnEAsexXwjvVVHP39KQCqfBO7lQ69x0NEUxptgzFiSfPnxcYhgWKRaShxcqQzRCytXiQJZgB1SjMocFop81ixSgJ9oQQJ1YwvGO03/wc7FhNNavz+SBf9kSTN/VF2HuzEH0kiigKhmIpVEQnEVHQjjqqZyZUzJ2ZWWWJJevWv3R8jkl5x7fBn180LJ3t53wJLydXuj3GmxxQi2N/ip2LNyOeKZcj69UVmLy28p3lg8pPyXFKUeCzUFrmQRLh1TQWiKMxIHnggkuTBV5pJaQbXLC+Z0cr8dBhSHzSAk919rK+ZvluRRRbZWFuALM584bbQackZ/zdVrIrEigoPoXiKZXOYKPpywB9NcazTTBC6d5ry70N+EWaMJ8RTI2qKPaEE//X8eb545+op3UsUBXyjhDJkSaQ2I8fdaAW4hUR+p2cUMnD72jLqi1y8eV0VDeWX54t3/oE75rsIeS4D5kvW4j1bqrP+vnNDNU8e6yEUV/no1YvHnC8y7DXG5lFSy4ORJLF03p9O/9xqDdtliGWEH1lE+MSNDXzjyVMk0wV6//Z6DAQkSWRRiZvjXUG6/FFkWabEZaXAqfDB7YtYVOLkbG+Y/3juLG6bzHWNY0UELLLIB7bX47bKtA5EONkTAcNUr6sqcBApd6PrBsF4N167wpvWVfCOjTUUu3JPVCq9NtbX+ugLJdi6OLveCl3z43OvAONF05S4rXjsCuG4OiYJ7VvWVPLwgQ4APnrN2DYyXb545xpu+8YuDMwcQ7nTVea5VHBaRL75no2srPLNeNL259cuASAYSw3HXQyEL27unoF+YAbiqtcsK+FwR4B1s8zvNRu8doV3ba6lMxBjfc38lWMh4rErVHhtdAbiLJvmvPSqJQWc6o0QSepYJNNzyR9VMTDHjJtXLixBmgvJG8Lo8YpwzepSTrT38Q93b0HXNYpcNpp7w5T4rKypKpzvIl4wXv3LTZSWvnEadJ7pM9oA/tl9G7jn+/sAeOuqid+NI6Ou/ckfNPHu7+4BYEvtxH7do5+79zM3cMVXngKgqXzszukX37yCrzx6nDVVHu69clnWsaoCB9+7bxPBuEpd0VhT7F/etY5/+t0xVtcU8PZN2bsj1QV2NtYVMBBJsHXxxJ9XBL5050q+8rtjRJK5XXauX1aELIloBvz5jct4aE8rjxzoYGmpiw/uWMwtq8p51+ZafrG3jeuWl1Lhs2OVJXrDphpDlc9O60CUUFylMxijwmNjZaW52lxX5KTCZ6fTH2djXe7V42KXlfuuXMS+lkHO90cQBYHtS4up8tnRdYOWgShv31BNOKGybUkh4gQru4IgcN3y3P3HV9+5efj38w/cQf39O3Oe9927lvEHvziZ89j5B+7grn99iqVVRTzwjvXjliOzrZya4FkOi8x92+tRdR2rnD1x/ad3rmVtrQ+rIvLuzXVTetZENFZ4ee2zN9DSH2FjXRHWr03/HnPNRN/DfJXDJpAWA8rNynInR7vGxkfd2FhCNJFid1oa/K1ri/m/g9lbywUWcNgUbl9byZIyDzetKOO/nzvFw3ub6R7l7VrhseBzKKQ0g+2LCkEQaRkIk1INvnbPBtw2ZVp5SA5+9kbWfvlJAO7eWM3ychcfvto0euqKHGxZVEgwnmLLJH1KJjNtN5VuCx2hJDYJrl8/s+DxdTU+1i0AQ2NpqSune9wbfaFWEgXetbmWlKajSCIrKzxMtt8jAD/72Baa6oowMN2oVd0gpWp0BRMEowmqChyUeBZm/M2FQDAuAV/b4uJiw1VcgT+aSm/JGQwV26ZIlHtsDEaTxFM6PocyRpI2ntIIJ1Sssog77WeY0gyCsRSSCF67JWcW99EE4ymSqo7bqmBVFkbszqkz5xDcJai6gUUSsCkSdouEL8dnSml6+jOLeB3KsAxuVyAOApR7bCiSSEI11VNEQaDca0OeooRlLKkRSarYFGmMeks8pROKp0hpOjZFwmtXZiyNOVXOnD2Ht7QSmyKlVWVUUppBUtWRRNB0sCoi5R7bmLJrukHbYIyEqmFTJHwOZdhHNZ7SCSdSKJI4HNd1IT+Pgekfruk6HrsyJ1r558+fp76+ftb3uRw5evIMuEswDHPQcFjMybOqG8PuG4YBgZip3mi2C4NgTEUSheF363Lk5JmzWH3l2C0SpaMkm493hVDTstqZakC6YdDpj6MZBqVu65RX0fvDCQYiSayKNCYwOpxQiac0nBYZu2VhuFKcOnMWi68cu0WkdAqJYC8URzuCaIaBIgo0VszPbpxhQPNAhJRqUOK20N/Vjre0Ej39TgViZjJgp1XGnu5f4ymdaFIlltTQDSh0KsPj9WgCsRTxlEY8pSOKI2PXeCRUc/yxSCKeUfODXOPixWT/0VNIXnNhYWW5C0laGO15IZCvm/E5e+48grsk/b6YL5ZVlobl4YtdFpwZ87BgLEVS03HbFKzywpi/Xij27t1rGIaR80NeEjs9pZU1XPXJ/2Jf6yDxlI6mGxiGMTwpv3NjJS+c7AdM94Zvvmdj1vU/fLmZvnT+jQ9ftQi3TeH3R7o40mH6R755XeWkgXeBaIrvvWgGrZZ5bLxny/Q16C8EpYtXUvcH3yAQTeGxyZR57dy4ooybV5WxqjLbF/mRgx2c6jb93d+xsZraIgf/u/s8vztkOma8eV0F79tazzefOcULJ80VtXdvqeXO9VNTaPrvF84Sipu+PH9y/dKsQehHLzdzuD3Aub4IG2p97Fhawo6G4ll//omob1zDJ/7tlwC4rDKheIpHDnZS5LLQ3BelutCOIol8+c7VPHeyN6vsL57u5W9/dZhIQsVukXlnUw0fvLIen8My3J46AzEKnRasssT2JUVsWXxhBCBa+qP88vU2AJaVubljbe4kYtOhqamJPXv2zPo+lyPOqmVU3vcvpDRQRHMg8dgVBAEaytz84zvWEoqn+O0B871ZXeVF03WOdZq5g+7cUMWiWSSPW8iULFrJ9fd/F4D/vPeKYd/uXad6ef/3XgXMgOY9X75t+Jonj3bxXy+Yfeem+kI+ecvUpKLf+s1d9KV3v/7rA02srDD7s5Sm882nTwPgtsl8+KrZu6rNBaWLV3Ldp8y6+bd3b6DUc/ENn/aBGFf+v6eH/94zT6vjz53o4TO/MgP9awsdvPwvH+OPv/4Qr50fwGWReK15EIsk4rRJ3Lm+mquXlfDa+QG6AjGeONpDlc9GVYGDf7ln/Zh7tw1GeWhPGwfb/LQMRCl2WbljbQXv31Y/bnl++moLnQHTdfW+7fVZSoQ7D3Zystt8d4fGxYuJtaKBig98HYAbr6jgn+7eOPEFbyAy62bzihL+8wObJ77gDcSixrVUfOCf6QwkhuNzXFYJq2yqCC8rd/Olt5pxOv3hBD94qRmASp+NezYtjPnrhUIQhNfHO3ZJmHsWWaSuyIk9nZncY5NxWmUcVplCl5Vti4qpS3dUuQIGl6UNmqoCO06LaectKXUhiQJum0yFd/LByWWTqUwHSC8rm5kyyYVgaKXMqkgUOC3UFNhxWiWqfWM77oZSN6Ig4HMolHrMVdqmOlOdxiqLXFFnbsNvqi9EkUScVpn10/DvbUgHHi4ucY5ZdWsodeGxKxSlVx9G+9tfCIbcWxaXOGmscCMIAsvLzTpYXOpEFkUqfXZqCu1jyt5Y7qHEbcUii1T57FR4bcOrjkPtaXm5G49NwSKLs8qQPBmlHis+h4IoCDNWxckzdWyKhCIISCKIokhVgZ1itwVZEllZ6aHAYaHca8dtk5FEgSUlTpaUuBAFM/FpmWfhJWSbK4Z2vZaUOPFkuAKtr/TiSO/g1BVmu0qsqvTitsnIojAtV58hFakyj41FRSPtXpFEFqf7j4YFFOw8VDf1xc5xY6QuNFWFduT0VoV9Hr0R1lb5KHRaEASBbYuLsclmDpfFJU7qCu147QqKLLK4yIUtvZO3rMyFy6pQ5bMhCMK4KmJDyXKrfOZ9rIpI0ziunkM0pMfsCq9tzE7P0lLXmHFxvvjgtqXz+vyFRuau2yduaJy3cixEnFYZr8OCIgsImK5rHptCldeOIMDmupG+1hyXzPnrQuoz54NLwr2tqanJeOXV10ikzG08KS0hbRimQWRVJHRdJ5LUxt0OT6gaFklEyPD5Smk6kiCMm3xvNEZaim+0r/h80tTUxDMvvIQkGOiIWBURURDGdbVKqjqymP2ZEykziNua4XYSS2qmtOE0AzoTqjZu/SRUbdi3+2JIezc1NfHiy68Ml2eobKF4CpdVJpxQcVqk4ZiG0WVPqRqRpIbDIqNIQlbbGWpPumG2iwv9eXTdMF0Y52hbeminZzz//zey/3RTUxO/ffIFfA4LwViCIqcNVddN11a7MtwOdN0w3YjS3/10+5NLkaamJp7Z9VLOfjaZTNI8kMgp/qKqOkldx2GZnnPBYDiJxy7ldGuZqK+ZD4bqJrNPmS9eO9vPpgu08zxVNE0jGNMocFmG+2KLJJLUdAQdc7y2ywgZ41VC1VBEgWhSnzC+Zqg/1HVTDcQ2hXaVVPUx/XjmsdHj4sWiqamJr/7Pr9lS58HjySfkzCRfN+PT1NTEiy+9YrpuxpK4LDKiJOKwSMRSY98fwzBIaXM3h1jICIKw1zCMplzHLgn3NjCDuCZKHCiKIm7b+F9mrsFxurERgiAsqEF2CLd96nLZuRq8NYdhM1M/+YnqZz7qLvOZQ78PTdhGT9xGl0+RJXzjlHnoXDNx7YUfKEVRwHIZT6YXGhU+c7fCbjF3TGVEbKMW70VRQMz47uci1upSYLyFJYvFQkN57h0OWRaRZ+BYUDDBjsmC7IsXSG6K+TZ4ACRJosA1tv8d+mnJMcYMHXNNMJZDZn849TY10WRvvieCN62pnvykNyj5uhkfa9rLZ/TupSvHWCQIAhY5P4d4Y4zSefLkyZMnT548efLkecOSN3ry5MmTJ0+ePHny5MlzWXNJuLeFE6lxYw8kAb53XxOvnR/k+y+ewyJL3LiilIOtA5zui2GVRa5qKOFUd4im+kLuv62Rwx1BbLLE44c7efDVZmIpgwqvjaf/4kqsViv7W/3EUzpN9QVZLitne8O0DppJs7x2hWeOd/PIwU7etLaC6xrLssp1oitE26CZKKDMYxvO6qzrBq+3DHKsI8C+1gDXLS/hzVNUR8vFkfbAcN0MlXRJiZ3b11SS1Ax+/moL/RnZEW9ZWcL62gK+98JZeiMqEuC0moH4oZhGIJ7kr29ZhkWW+e3+dva2+FEkkT+5pp6vPXmaaMrAbYFVVT4kUeRdm2vZuriYF071MhBJUuRQ+OW+dm5cUcatqyt47HAnxS4rN60qy+mO0joQ5WxfhFWVHopdVqJJlb3NgxS7rCwpcvK1J0+SVDU+deuKcV3ujnUG6QsnuKKuICtmIBRPsfJzjxJN6YiYLgxbFheyuNhJXziJP5rkZE+Y65YV0RlIsOf8IFZFpLHcjSRJ3L66lJ/vaScQS9FQ6qQrmOD6xjLWVHn41rNniCQ0GkpcVPisWBQZuyzwnefP4bBKrKz0ous6Hf44LqtMbbETwYAVlW5OdYepKrBxrDNEMJ6idSBMy4CZwK6uwMrSMg+SKLDn/CCaobO6wsPmxcUoskjrQJRVlR5uXVXB8a4QrYMR/uPZM/SHExQ5LRS6LJS5rVgUiboiF8UuCy+d6ac/HCeh6qytLuALb51a5uU3Kocy3ikwnReHIh/XVVipLS4glFA50OonmtIQDIOEBpIIS0sc/NmNyylyWmmqL8yKrfNHkxxoC1Bb6OBsT4jv7T7PjY2lfHBHbvWxoXdjdaWHItfCEEfIrJvRcV9D/3fIcPTL2ccW3b8TA/jCHcv4wFUNw/9Pqjp7mgewKxIbRonQbPrSY/SmU82Pftayz+wkqUOpS+HVz948pbLHkyoPvtpq5unZVJMVd9Ppj/HwvnaWl7m4ceXU85wc7wrSG0qwsbaAMz1h6u/ficsicviLt01+8QUis+3OZ2xeZjmGdDr/d/dZfn+0hxdP9w8fs0kCNzcW0xlOcbI7POxu/de3NHB3U3Y+pX976iSPHu6irsiBqGs8dqwPQYCdf3I1hW4LRzuD7DzQweGOAH9103JuWGmOy0c7A3xv13k21Ph479Y6vrLzKEc6/MiihM0icff6aiqK7GMUTyciGE7wtv94ibiq8tb1lSwv9/LWGYzlE71TF5OfvdrCbw52cPcV1dy5YWG4lC2UulmIGAas//ud+BNjj9lF0AxIGlDklPnXd21gT7OfvnCCkz1hVlZ4+NStjTNKwnukI8BgJMUVdQWThkGkNJ095wexKSLra3w54+kuNpeE0XOuL8p4Ir2aAR/6/h4EQDWApM7P97YPH1eTOo8d6UYAWgaiSIJAicfGK+f62XN+ADWdFb0jEOfd393DP929jmdP9A5fv22J6RsdSaj89kAnumHQG0pw1xXVfHnnMeIpjQOt/iyjZyCS5NHDnZzpDaNqBsvK3BSk1WaOdgZ54VQfP3mlBR2Dvc0DXLe8BJd9Zmo/eo7fT/XG+M4L59B0nXSi+WEeP9rLcyf7iKvmNE4Dggmdg+2h4XM+939HaazwcLg9OFTLfPHRU8PHQ0l4+ZwfiyTQGUhwzXI/RzuC6LrOsa4QAmauiNM9YU71hFEkEbdd4ZplJVllUTWd/9vfTkozaOmPcO+2ep4/2Tss/atqOjsPmhnUbbLE/bevGPP5e0MJHjvcBZjf0a2rR1pK60CU0pQ+XDdxVee5k328cnYAAYNYug5+tqdjeFIbSensPjuIRRJ49Vw/Kc1AN+BsXxRJgDM9YexWmYGwmaPlVHcIh9VUFewKxNEBf1ylK9iDYYxMlve3+bErEo8d6cJukYZzTOi6kfUdNg8maBnsJVNe5MWzgxxoD+G2SkSSGvtb/ZzsMuv1ob0tBOPmlxxOxmkejCMJIEsCDqUfRRYZiCRQdXPyfrY3ytKLoJx3OZH5XRzoTHCoqyvrux1C1eF4d5QvP3KMu66oRpHFLDXJ3x/ppt0f40CrnwdfaSaSUDnSHuBtGyrxObMVJFOazq/3taOmk4neu3X8ZJrzxdbP7eTlL5kTkQ/+1+7h/0fV7PNW/92jw3X19ztPZhk9e84P8Mq5AcCMh8lUJxwyeADu/Nfn+PWfXQPAswe7SaZfmp5wasrl/cXeNh4/YvYVXpvCm9ZVDh/79nNnONEV4oVTvSwv81AzBdnivnCCRw+Z9wvFVaIpDS8QTup8++nj/NH1F19t6uf7j1z0Z+Yi1yKlPxLnX544hT+W3UDimsFvj2T0eenUAZ/79ZEsoycaTfHvz5xBTcvDD59vwFu/9QL337aC450BfrmvA1GAT/3yIHtW3gTAF39zlNbBKC+f7UfXdX7yaguxdP9rkUVa+qPcvKqcIqeV8imouQLc94PXONNnJlX93q5m1tX4qC5wcMUkSnITUX//znmb3D/w2HFUTedEV2jBGD2ZzGfdLEQ6/DGsOQwegFjGpKI/ovLxH79OqcfO+b4wIHCk3Vx8++CVi6b1zK5AnN8f6TafkdK4aWXZhOfvOT/Iy2fNBQ63TWZp6fwrx10S7m2T2YaKSFYizvHOFwQBj92082yyhDjK6ix2WbN2I2wZkp+SKCCbUevD/x9K8DQ6UakiCUiCgCyKpiKMIAyfO3StLJkyg4okYrkACbckUUQScn+9E2XHBlBEYUxC0vGusCkibqs8XJdKevVUkgTc6bqWRQFbjkBRURCGA0iHVveGfkqiQFFGLgWPI7d9bpHE4dX00YIM460qiGJuBZ+sawE5hwKTLIlYM74vQTA/nyCYK/2Z12c+QhRF81xJQBTMzycIuet1dNGG5CiV9LWyKOJKyyXnUo0TBAEBYfga8+/0MVGgcIHsGlyqTNZp2hTzu7aN2tkc6icUSRz+XRJFbNLYtp35buR6dxYClRmx8hPJdLsnUOHKfGdtE0gsV2ZMRAunrnqdhStDZGB0mYZSGciiMOXE04okDveTo8s+OpnqxWLVwtBRyIlNkpHGUbXL1R3Lo8ZFRWFk53TU+TZFwqZIyPKItEjm9ziUpFEWBUrd1pE+UTCfPaR6Oh1Bg2LnSJuX0uP8RG19oTPk1WJ5gwiyXOoMzUengk2REAVz7oNgji/eGbRViywOz/Um6q9HnjtyzkIRnrkk3tCVlR4GMv6u81lp8yeQBPA4FB79xDUcaR/kHx87gdehcOeGSva1BHjxdB8+h8I7m2p48XQfVzWUcM+mWk73hnn3pmr2nOvnP54/R+tglE31hfzXBzYBcNcV1SRUjSUlI6uONkXiXZtq6A4mhlcjv/mejTx2pJNbV2XvQ7ltCvdsrqE3lEAACpwWitMTzaWlbt62QWT70kL2Ng9ybUNpThWbqVJgH/kKCx2mPtJ1y0u5dU0F0aTGj14+x6H2APEUFDhE/vj65SwpcfL9F8+y+8wAxS4rPrvC1sWF9IaTtPnjfOmtq4indHad7uGJI91YZInPvXkln//1IY51h1lZ5uSqZaXENTMR45ISF0c6ggxGk9QU2PjJq63csaaCFZVeXjs/QIFDYV2OfD+iKHBPUy1t/uhwXV/dUEK5x0aR00Kpx0aB00Jc1Xj35twr3V6Hwrs21dAfSbJslP78khIXlWUOOgdjWC0yXrvCHWsrqCty0RWIo2oaL57p5+6mKtoGYjx+uBu7VWLHkmKiqsZb1lfx89da8UeTrCxzcn4wwU0ry1hS4uJ7L54joWosK3NT6DDz+ZS6Fb7w22NU+WysrPIiCXC6N0yJy0Z1gQNV11ld5eVwe4CGUjfHOoP0hxNE4il+trcdtwWuXFbG8nIvkmjw3PEe4imdaxtKWVvvQ9ehJxRjUbGbproCzvVHuG1NGf/53BlaBqIsKnJS7LJS6rEhiQL1RQ7cNoUDbea2djSps7HWx1vWV/HFGbe4NxZeq0hDmZs9LQEAPn7tYgqdFoKxFC+f6aU7lMRnFTnTH6PQZWHboiLu3b4Yh0VicUl2TqVbV5dzuidMpdfObavL+J/d57h9VSW2HIOPJAq8a1P2u7EQyBxmH/7zkVXXB+65gt8c2klUhTetzN7RfekzN3HFl35PMJbilU9uyzq2sdaHxyZjUySqC7INhQ9uqeJ/XmnHqcC3PrBl+P9rq8tYXeHkcGeEt6+beKUxkzs3VOFzKNhkia1LstXNPnHDUp481sPSUteUk4p67Qr3ZPQ95R4rkgDrqtzcsX5+kv+tWrUKK+dJALW++ZtknH/gjjG7PTabzLfv3cjOgx387+6W4f83FNu5e1MdkUSSx4/2UFdgpz+a4qtvW5N1vaIofPO9G3j49TaaagtJ6Qb/9fxpJEFk92duJJbUWFPtZW2ljxfO9HL/LSOeAV+7ax0/29NCU10BTYuKSOoGB1r8uO3mgt1Nq8rx2S0UOqfucfGdD2zizx58nXAixT2b6ij3WseMQdNlPncy/uveK3h4fxv3NC3MxJX5XZ5syjw21jUU8tSpgTHHVpQ7Saga3YEETfUFPPD2dext9pNUVQ51BFlV5Z3Rbl6h08I9m2rwx5Ism8KuzfoaH26bjFWWqJmnhaDRXDJ5evLZ43MzlG8lz1jydTM++Tw945NvN+OTr5vxydfN+OTrZnzydTM++boZn3zdjM9EeXry+5h58uTJkydPnjx58uS5rMkbPXny5MmTJ0+ePHny5LmsuSRienTDYNVndxJRwSFBmddCuUPkpbZ4lpwswLJiK99631aWlrvoC0X5wUstXN1QgsumEIqnWF3l45GD7ayo8PCZhw+ypNjFrlO9BKIqG+q8fPqOVZzvCfPj11r48I5F3LLGVPg50DqITZYIJzU21Hiz5E7Hoy8cp3UghpKWD6wvcuF1jI001TSNp4/30ljhpqYwW1lrMJzklfMD7FhSOK7C2z/v3M+/vdBuxvMs8XK8N0p/KIXXIeCPGDhssLHGzZ7WKNUFVgrsVgrcFjoHo7zWYiqlff2edSwvc+OxK8RVjfO9UbqDMdbXFGAAvznQzsZaL08d6+FMd5jBaJytS4r54+uXE0qoeGwKTovM8yd70HSdxaVuevxxnjnZy8pKJw3lXq6oLeR8XwR/NIkoQEOZh73N/QTiKa5ZVoo34/Ptb+7n8SPd1Bc7WFzqZlN9EV2BOE6rNK2s5+2DUZ461sXvj3TxyplBUsD6ag8SBinD4ObGEgbjuqn05rbxoasWkUjp7G/1I2JgsyisqfLSNhjhWEeQaEJDUcAiKywqsHK4K8SyMjcSAg/uaWFTXQH+qEospeKwyKyqcPPsqT7a+qN0BmK8fV0Z77uqAUWSePj1VoIxldoiB4ooMhhNsrLCzYG2AO0DUd7eVIM/msIfTbK41MWRdj/Hu0JEYkkG4hoba7yc74tx+9oK+kMJOgIx+iNxlpa6WVHh5Zd7WokkVd7WVMn/vtDC+lofH9i+mIJp+K2/EcmUgfdaIKGBR4EUEvdduYjtS0soclv4l8eOc6Ddj0sWOdEbQwNKnSL/92fXcqo7xPJyD6VuK839EQKxFMVuK1U+069Z0w26gnGKXZYLGuAZS2oMRpMUuyz0hpOUuKyTBmurmk53KJGzbJG4yvovPM4fbK/hT29amXVsyf070YCPXSnx6TffmnXsyq/8nr5wihNfGes2uf2rT1DpsfGLP74q6/93f2Enr8XM30e7W371kUP8z+4WvnXXRm7cmB1Tebo7ROtgdEwagYlIpVJ878Vmti4pYl3N1NW3Drf7OdYZ5O6mWoLxBPX372RTtZuH/uTqKd9jrlloktVDZWj3xxgIJdh5qJP/eP4sAF9/51okUaS2yEEyqfJfu85zfWMhrQNJPnHDEiwWs6/SdYPOYJyTnQG+9+J5PrS9Dl0Q+PD/7gXg3AN3sLe5j7956BCrKlwc6QzzmdsbsFqs7Fhagqqq/PL1DhrKXPSEknzywb2Edbh6SSGqrnP/HasocVmp8Nn5ym8P8NM9nfz8D5torCjO8clMpdBgPMWbv/EccVXjY9c2sLLcww3TkDsfYi5kmR871M4nHzrIH11dzx/fOFbl9GIRT2n0R5KUp+NKZ0tesnpi/t/vDvGt51vG/L/ICmtrC1he4cUwBK5qKOJYV4h11QW47QqRhIpNlrDIAuU+O56MOVVC1TjWGaK+yIHPcfnNFS6JmB5rRYNR8YGvT/l8AbMT3PwPT9IXTiAKAtUFdnTDQDcM/NEU4YQ27rWZNfLMX17LMyd72Hmog3N9UeoKHVzVUMxf3rx8wjIEokn+4uf7Od8XJZxIYZUlrm8s5Y+vWzomUPYvfrafV8/1Y1ckHvzIFko99uFjd37zRXrDcWoKHPz0Y9tGP4byJSux3f1PU62aCfHaFdZUeWjpj9EVjKHqBoUOhUA0RWqcZuK2SiwqdpqCAymdfS2DaIZBocNCXzg5XJdeu8zdV1RzqD3Iqe4QRekJ1bm+CIZhsLrax8/Tn++Vs/184HuvEk/riZe4LNx1RTUWWcIii7xvax1e++SGz5r1G6m67+sc7wpNeu7I5xGRRJFQXEU3TEU2t1UhmlRJaLkrYXSbmYxStxVZFOgIxLPuwaj7yKKALIKmg1UWCCd1xmN0GcYr09oqDz/92Dau3r41H9MzDtaKBibqb+wSIMAo5d0sqnw2Kn0O3re5hodeb6N1MEZDqYuPX7eUjbUF/PZAB6d7whS5LNy7te6C5C9IqBo/2N1MOKESTqi4rDIVXhvv2jxxoPKv9rVxvi9KidvK+0ZJZWfWze//fCvLyk1BgLv+dSd7OkbOy2w/a/9+J8FE7mND+XsAKr0Wdn/6puFjo9vm0HWdnZ1s+8brOe93tDPAx3/0OilN5461lXwmh8x9Lu741xc41R1ClkR+9UfbWF4xeb6Wox0B3vvfr6BqOpsXFfKjT793uG4euKORd121ZErPnmsWgtEz+ruz/PYzfPifHuQ7u5pR9eyeSRIFytwWOgIjjUQUoNxrY/f9NwDw5NFuDrUH+MZTp5gOVT4b79pUy67TvRztCBJN6Wj62J5RBK5sKOa2VaV85tdHh/+fq/6iSZUfvNTMA48ez/q/LAr8893reMuG6eXqGd3fzOQ7y6zv1/5qMyUlJROcfWHQdIMfvnSewWiKxnI3t60ZL9HI1JmLurlcqVu2GuHt/zjl80XMd63EYyWVzmFR4bGzeVEhH79uKYVOC4Zh8A+/O8ahtgAlbiv/cOeanAv1C503XEzPUJcWips5HDTDIBxLoOkGgWhqwhnq6EMnuwO0DUbRdYOUqpFUNdoGY5OWYSCSJJLQiKsaiZQ+bGwNRJNjzm33m/eLpTTaBkcmwpqmMRA1B4LecG5B9lhq/InwdEmqGoPRFJGkag4MhlkmdYL6iiY1EqpOKK6aeWoMA0OHcELNqktNNzjWGSSSUElpOglVpy8UR9UNNAN6gyOfu3kgSkof+VyqbnCyO5Quo04wNrXcHClNH24DUyWW1ImrOumPj2FANJkiNY7BA9MzeMBMtju6XEaO+6i6gaYbGIZBfJLvefS145WpJ5QgONFsPc+kJLSJDR4w2004nuJMX5RwQkXVdMIJlb6Q+R73pd/ngUhyzCRwrognzWeCmdMh87kT0RdKDpdNn6Bsjx/qHv490+AZTXCCR2bevSswtm/MxYtt4593pjtMSjPflfPpHCpTYaj/UTWdY53BSc42OdkdQk0/q90fzzr24N7WKT97LhlvEWO+UXWDaNJAy7HIaugGkVGLkLqBOVan6Qsnsv6eDmf6wnQHExiQ0+ABM49bUtV55GB7zuOZhBMqsdEJ8DA9Ul5rHqukdbHZ2zW192iuSWk6/vTYPN58Jc/cEcnRBifCwJwLRxMaSU0nldJJqBqhuIo/PS9VdYPu9OJDMJYiMMW51qXEJeHe5rNPr5gryk0Xsfdvq+Pne9qoS2daDsZUGitcPHKwi1A0RVsgPubaK2o9nOqJEoyrFDgUbllTybJyD7oBVQUOilxW7r5icqm/RSUu3rK+kn0tfhIpDVEQeNP6ChpyyPz92Q1L+c7zZ2ks97AxI7GZJEl86MpFPHW8hzeNs2pSW+hg6vsYE3N1QwlbFhfS0h/h1fODDERSbKr3MRBOcLA9iMMi0RMa2b0RgPdvrUWUJKp8Nrw2hW89e5qEZrB1USG7z/TTG4zjtMmsrvbxd29awWOHuznQ5qfIaWVlhYudh7qIJbWs5Iu3r67g16+3caA9gD2d4PFvbm3keHeIAoeF6gJ7zvKPxmWVecfGar6/+9xwAs/RDFn9Oubq4juuqCSc0NnX4kczDJwWiY01Ps70hTndEyalpfM6yCIWWSSW1ChwWIimVPrDKSRAkARUzUAWwWYRicT14QSkEvCuphocVpkfv9xMNKnitVuQRIGUplPittI+GCGuwpoqD4bB8MrZK+f6CURVtIx7IUCl10pKA380gWqAyyZT47VxpCsMgEOGSAosksCfXt8w5eR7eXKzY1kxlU6Bn+7rzXn8nas89GpWti0q4s6NVdgtIie7w6yr9rFlsbkzcuOKMl5vGaSh1D2cH2Ou8ToUrlleQttgjB1Li+kIxFhZ4Zn0uptWlnGgzU9jucfM65DBUI4vEbLc23JJFA/x4EfX8p7vHARgZVm2bOlb15Tyf4d6APjRfVvGXJt5/yHu2lTHJ3952CzPqPNuX1PO7jP9dAbj/PkNDUyVv7ppOd94+hSLip3cubFmStfcuaGaxw530TwQ5VO3NPJMxqLrb/782ik/ey6Z6HuYz3K4bTIb6gs42BHg9RZ/1rnVBXY2LSrk1bN9dAQSyCI4LAofuqp++Jxrl5dit0g8esRKTzBBoVNGS6r403OyHfUezg7E6QiOTPg9VokVFR7+7PoGjnQE+PdnzmCRBLwOCy+c6ssqw5ZaDzeuKOUPdixi8WceBcBnze12Wuq2ceXSYs71DPKbQ+Z9JKCuyMnnbp/YA2QyZrqTYZUhoZpj8q1rprfTNFfYFIkbV5Rxpjc8qwSt45Hf5cmmptDB4CTnSIKZW6fEZSWe0ij3WllR4WMwmsCqSFR47WxbXMiiYnPOrEgi926r43eHOllX7aWmcGpzrUuJS8K9LS9ZPT552cLxydfN+OQlq8cn327GJ18345Ovm/HJ18345OtmfPJ1Mz75uhmfN5x7W548efLkyZMnT548efIMkTd68uTJkydPnjx58uTJc1mTN3ry5MmTJ0+ePHny5MlzWTNvQgaCIJQC3wWKgecMw7h/vHNPdYfmJDjTbZNJqCpJFRQRRgtiSQKIgoBdESlwWih0Wllc4mR1lZdEUuXHr7USjatsX1LI6b4oVkni6+9ax2A0xavnBlhW7ua65aWAmVfg0cNdtPujXLe8lIaysQIGmZ/vmRM9VPrs3L66Ykzw8ERk6tjPFZnyyRZJ4JplJTRWePjB7nMEMgQBZCBTxEoGEAVEAUo9VjbU+AglNOqLHaRSBr98vZV4WgquwC6T1HREUcRtlVBkkUqfnTWVXn76agvBtJrP4kI7W5YU89k3rcRpNZtrJKHyf/s7SKoab1pXSbHLmvNznO0NL4ig3okQgbnS3yt0yFlCB05FRNUNkpoxLD7hskrs+9xN490iD5O/U07FFIYYDxFYVOLg/7t7PX3hBN98+jSKJPLXty5n8yJTyOCD//MqL5/tp7bIweN/fs3wtSlN5yevtLDrdB9rq3188Mr64XY/XV5vHuCHLzWjyCJ/sGMxy8vH74OmykR5M8aTSh5Pehrg//v9Mb71zFkkUeD7921ie0NJzuu+vhXuvDP3PTPvl1R1fnugg8FokltWlVNTOCKc8OH/eYUnT5iB5x+7qo5P37F6+NjKzz1KND0gPPtXV1Jf4puoGgBoGwxx0z/vIqHq3LyybMHkFFkIktU3fWYnpzI6tu6Oid+pXBL7DaVOfvThrew82MkPdp+jZTDGREKHPruMP4es4vYlBfSFU5zsNoVdMsctpyISV3WGxDm/9KZl7D4f5GR3iLdvrOKPrzPFMJ482sWDr7awqNjF39y8nMeOdvGZn+8nMqrz/pd3rMDtdHKmN8z2JcWsqZ5c+nx0fzOT72yo/UoCnPnqzL7zv334IC+fG+CqhmI+/5bVk19wARg9b5qLurlcOdM9tbmfyyJS7rMTiKZwWSUEUWTHkiK+eOcafn+ki1/sbaOqwM5f3LQsK1/P5cp87vTYgfsMw9gGbBYEYdxMckP5WmZLKG4aPDDW4AEzgWhKNwglNLqCcc71hTnXG2HX6T6OdIToDsSJqzpPnegjGEvRG47zm/2d7G0eJJrU2N/iJ5ku62A0ycnuEJGExr5RajWj2dfqJ5LQONUdzilpfbHJlE9Oagb72/w8crAzy+CBbINn6O+hSXZPMMG+Fj8dgzEOtwV5tXlg2OABGIypRJM64bhKbyhBbzDJme4wJ7pDwwYPQEcwzustfs5lyM+e64vQHYwzGE1NKC87XUnH+WDuBMdhMMPgAYikdBIZBg9AOKGx63TPHD71jcdEBg+Y32lfOMnD+9p59FAXvWEzcewTR0cknl89N4CmG5zrjdA+EB3+f6c/zr5WP4FYiqMdAc70hmdczl2n++gIxGnuj/La+bmX0v3ML0Zy5cx0ceHBV1vRDLOf+ecnT4573p+/PPL7js+N/6wOf4yWgSihuMqh9kDWsadPjCh2/c/u5qxj0YwB4U9+cmBKZf/Os+eJpUx5++dOZiv5zddiy0JZ5Dk1qmObTJU91+HTPRGOdAQIxFKTGjxAToMH4OUzg5zqHnmPMs+KpEYMHoDPP3KSg21+4imNRw91Df//8SPdRBIah9sD7Gsd5GxvZIzBA/DZ35zgUHuAaFLj9ZbJtLXmjqH2qxmw53TXJGfn5rlTfcRTGs+eyK1KeTEYmMa86Y1OdIqZJ8JJnea+CNGkSstAjEhc5flTfcSTKk8d6yYQS3G8M8jpnpmPNZcS82b0GIbRbBhGf/pPHciaSgiC8FFBEPYIgrCH2NTyJkyGLIx84FwfXMCULbbKIh6bQrHbRpHLwppKL3VFDtw2BVEUWFflRZFE7IrMjStKWZGWgW0ocw1nO/faFap8dgQBGismXmFtLHcjCFDps+GbQtLNi4kkwqJiJ9uXFGJTsmst136UKKQTetpkFhU78dgV6oocNJS6yFTmtcsCFlnAIos4rTIuq0yp10alz06mUqjXobCkxElNwciKbU2hA7dNxiKLLClxjVt2ywWSAl6oOCzZn1eRBKRRX5JFEti86OInrrucGF2nuXBaZW5eWcb2pUU4LDIeu8LWtFw1wLIys92WuK1UZexGlHqsLClxIksCNYUO6gqdMy7nxpoC3DaZIpeF1VWTrzhPl09cNSKNu2qG97hueYnZZwhw37b6cc/7wBUjv+/60virveVes8+WRYFlo3bXV1SM9BXXLct+B+SMV+dv75haQtN7t9WhiAKCAGsqs+v3iyvHuegCs1BWwuei5y31WGkodWORRYqclpzjTSY2OfdTl5U6KfPk9gYYNaTxvi2V1BY6EASBbRnv69bFhQiCmSJiVaWHMo8tZ3k+dnUdi4qd5rg/BzurUyWzT2paWj6je6xJ9xHranxzUKKZ4bMrVPpsU5o3vdGZ6v6/RYIil5kWo9Bp/lxT5cVmkdm8qBBZFKjyOagvmvlYcykxa8lqQRBuMwzj0Vlc/6fACsMwPj7eOU1NTcbOx57glZMn6QkLuEocvLqvmduvXEFbMMibli0jmEjwxKlT3LNmNSfa+mkod6IKFlxWKwOxGNVFHuJxFVkWicZTeFxW4nGVnnAELRmjvKgIWTS3VxEkJFEYdjMTRQFdNxBFgXhcxWaTSSY1JMnMpQNm0jMph1va0HWTMdXzctQN//rTX+JPxLFLVoxYlGKvi4SuU+Vx84tDR3jLsqXYrBYOdnQhWCysLCtHMGQEUQPM5KAeqwW7VSGR1LAoEmCgqgaSJKAoEqIokExq6LpBKJ4wjRaLBRAIRGM4FAsWq4Su6RgGWK0yhgGGYSAIZl2qqk44lsRhlRGEkToVRAEMsuq7azBMeYErXR5xTMZ6wzAwMq4Zr25+/9RztAdCOC1WomqCUETF57DgsFlwWS1IooQsgGoYqJqGQ5EIJVLYFYVALInPYSWWTCGJIjargqCDbuhomopugMthHU7yJQoiuqEzGFUp9dhQJOgKhvEoCnabBV0HfzSGzWoB3cBqUbAqEsmkhqqniKd0XDYrSVVDFAUSiSSGoeGw2RBFkf5wBI/ViiBJpDQNiyQhCQIJTcOuyEiSiCgKhKIJbIqCJJkTsmRKQ0AgkkxR7HUM101esjo3TU1N9N34BZ742Gqe6exhe10dg2dPEEwkuHLLDnxuG6qqc6YnwrH+dppKCvnVoaOsqqtjR30tgmC266G+QVV1xIz+ZIhoNIVjnGzXqqqnv7/p9wmZDCUXnUnfkoumpia+9+udrK3OvTFff//OnG3nhz/cyb4U/POHxh4LhhNYFbBax05M//6hnXzh7txt8XM/28mX7sl9bLz+tK0/iEcBj2dsvqLzvf4pubWNJhhO4HFZaWpq4qo/+gL/8gfz/+6M9z1cbP7gX3by3b+4g6amJn79+NNUF3mG62vH/Tv5zd9sA8Bmc+NwKHz90QP8+W3rGAjGKPSYOUJ03UAQIBRJ4nFZ2XWmhR1Lajnf66fz9DEAtm0z79PSF6W22EHHQAiPRSCcgvIC09gdumcgmsLrUGjrD1JVaE6sD7X3ZLXpZFLDYsnO06OqOnKGYaXrBufOnQVAcBeyuLQg69hU37mh/ma239fprjBLy8dfBJwKuT73fDBUf3NVN5cjTU1NPPHkM2x74Hk04N41EE/CulU1NJaXUO3xIMsSbofZryZTGjarPOY7VjUdSZz9WLOQmEiyei6MnpOGYSyb4bVfBRTgbwzDGNfTJ5+nZ3zyWu3jk6+b8ckbPeOTbzfjk6+b8cnXzfjk62Z88nUzPvm6GZ983YzPREbPXAgZPCkIwu+AXwPDToGGYTw4SaGuBhTDMD45B2XIkydPnjx58uTJkydPnpzMhdFjB7qBbRn/M4AJjR5gO3CrIAhD1tgXDcN4eg7KkydPnjx58uTJkydPnjzDzNroMQzjgzO87gHggamceyFkmaeKBDhtEqG4hkE64HZ7HZU+Oz95tRlVM1hU4ua65SV88+nTlHltfPSqxTSUu3nHv79IJKXjVARuWlXB19+1Ycz9z/WG+cFL5+gLp7h9bQW3ra6YtEyvtwwSiqtsWVQ4r3UzEYU2gVgKYmlpHAmQZUikFUckwCILabEDC32hGKf7YgDYFIG3b6jm79+8ir/+xQEOtPhZW+NlaambwUiKp09047LK/PzDW3E5LQAc7wzy2JEu1lf7uLbRlA0/skDrZj5RBDg1Q0nTNwqzfaeG5HdFYPviQgZjSY51hRENsFtF7IpENKkSThq4LCI//shWnjrWw46GYiq8dk52h1hZ6aHCa2dv8yDhhEo0nuKpEz1sri/izg1Vw4IpQ+w+3cfLZ/u5eVV5lmiBrhu8dn4AVTfYvKiQWEpj7/lByjw2VlZ6ONcX4WxvmLXVPkrcuYO9D7UF6Isk2FRfOCPJ6omOZf7/s0vhwx+eXJZ6smO/2NNKRyDOe7bUUOyyDf//hi/v5EzaF+HDW+GzU5DAnoyh69wynMxLVo9bDp9u8PTxbj70/em543z5zhV85/nzxJMq0aRGODl9vcsbGwrZ1+6nPzr5tW9eW8Gzx3sIJzVuaizmH+/awD8+dhynTeIztzUOx+lBbqW8qdb3X/x0H0e7gtx/W+OcSJ0vun/nsALeTO/xhd8e4rHD3bx9fRV/fdvUxDwuBI8e7uR4Z5C7NtYsGBn4hcL3dp1jf+sgH7lqMcFYckbjVG2BBY/NQiCeoj+cothl4aE/2k5ZOoYOoMsf41f7Oyh1W3nL+kqUy0gUatZGjyAI5cC/AdcCGvA48BeGYcy9Ruo8oAHBDKlmzYDvvtiMyyoRSZiGUE8oyfMne9EM6Akn+e9dZ0lqOpG0jGQkZfD4kS7+47nT/OE1S7Pu/63nzvDciV5iSZV2f4zVFV5qihyMR3N/hOfSkpL6ZBqe88hAPLtsGqCp2X/HVIOjXeExOZPiKYOfvtqKTRF59FAnqg7tgRhuWz/xpEZM1REF+ItfHOC/PrAJTTf49rNn6ArG2dcySEO5myqffU7loC8XUgZ849Gj812My5qhlq8Du86OdIM6EErohBIjLTOc1PnbXx7Aabeyr8XPlsUFpDRTlv3GFWU8f7KXaFLlyWPdJFWdIx1BqgvtbF9SPHyPpKrz78+cRtUNTnaH+I97R1yZj3UF2X3GFMlUJJHOQIyzvab8e5HLwm8PdKDpBh3+GPfmUE/rCcZ58pgptR0fJQG//Ys72f135kRkosF3qgPzl0/Dhye4x9CkZ6L77W0e5KG9bQAkVJ2/vmX58LEzGYqs//0yfPbOyZ81EZnlCKljj83HJG2hLPKMLkcwluKBh6cff/BPj58iGFNzSlpPlSdPTX0q8tuDncO///54H2VPnuCF0+Z4W+Vz8KEdiya8firf+86DHew8ZD7n0788NO3rc5FZPz994QzvumrJtO/xw5da0Q2D7+w6N29GT2t/lP/dfR7DMGX/M1ko4hzzxYmuIN978RyGYdATPEbzQIzJl8jH0jKYBJIZf8f5y58d4Mcf2Tr8v++/dJ59LX5EQaDSa2Pb0uIcd7o0mQvz7XvALqAaqAf2AP85B/ddsEjCKDlkQUBJa0YKgNsiU5axwgimelKVz85ovHbFVIoTBGyKiN068Vdit0iIaZWNmSYtXGjkEg0RgRqflSEBHEkUUCQROUObs9Jn1rEogMtm1oVVlrAr868+s5BZXDa+UZ3n4lPuNncr3XYZl9VUc3NaZRzpd10SBRzpNm2VRVyj3ntZBEdajcc9SvI+81ynVRr+2yKbO05D78p4fYlVlpDF3P3N1obZKUVdCHwOZVhF0+e4sP2j4/JZ/LzgSKJAkW/6FTaeDPXFoijtSSAIAqUey5zcs9xjGx7DR7/Lc8GmypldN/TeyHOk9DgT7FZxeFfBu8DSd8w3XrsyPP9x2+e23ZR5s3f5CxxmW5clYXhudbkwF5+mwTCM2zP+/jdBEP5kDu47jPciVbpTgVKPne5AjJgKFW4Fq0VmY62HXacHSKgp3DYb/33fFWiawI9fbsaqCNQUOLl5ZTmf/+0R1lZ5uHlVOaUeO596aB8vnO7jmqXF3LquijevqxrzzL+8aRnrq32E4ipbFxdmuWPkotRt492bawglVBYXOymwX7wJvshIMs0SGyiKREdoZPV3VZmV490JbArcvKqccCzFrjP9OBSRUq+dAofCnrN+VGBrvZOoKnH9ilIwBBTB4JvPnkYUBJaVe/j4tQ3csLIMr8PKy2cG2LS4kEXFToJxjSeOdFDosA6vRgmCwP23NfLKuQGWl7kpTA9UVV5rfrdnFDc0FPDmjfV8Yb4LcglTVyDRPDh+4tu15Q46QknsisyfXLeE/kiSn+9tw2URqS5wIogCJU6Fn7zWxi0rSvni29axp3mA9TU+nFaZdn+M2kIHNkXiXZtriCRU3rO5lt1n+1hf7aOxIjsnjCiKfPltazjQ6mfLosLsshY5eeemGlRNp67ISWO5h9pCB0UuKx67wrs219AZiFM3zu6y16Hw7i21+KNJFhe7svKa/PO7rxn+/fwDd0yoBDiVYxOt4mYem+h+S0pcfPZNK+gKJLh2Wfbq5ETPKgV6Ms6bCke/MnK///eueu7939zlvZhMVDfzWQ6PXeEL79jBzf/y/JTvccdqL5+8dQPffOoUTknkUHeQfa2BnOe+d0stx9sG2dseylmW7z53mi89emLSZ37+zSvZc66P3WcG+Na717J1WQXVhQ68NoWbVmXnv8lV11P53q+oL+Sf7lrLS2f7+dStKyj92vSuz8XVi3w8f85PjVdmyZLp7/IA/OBDm/j+7vN8eMfiGV0/FxS7bHzxLas40xtmx9JiPpdx7I28ywNQ7rXzb+/awN7mQe7aWMW+f/XSN/llWTgVeO/WOiySRCyZ4kBbiMYKD1946+qs8z6wvZ5VlR6KXBZWVMx9jrf5ZC4kq/cC7zYM42T67wbgYcMw1sxB+YC8ZPVE5GULxydfN+OTl6wen3y7GZ983YxPvm7GJ18345Ovm/HJ18345OtmfC60ZPVfYcpWv4rpWrod+NAc3DdPnjx58uTJkydPnjx5Zs1cqLc9KwjCOkzJahn4uGEYvbMuWZ48efLkyZMnT548efLMATMyegRB2J7j3/70zwZBEBoMw9g941KNoj+cmDM/5VqgJf271yoSSIyN+rCJZnB9Y4WbxnIP62p87G/18/LZXkIxlV/98XZ+8FIb4WSKukIHjRVe7IrEz15r4frlJayrLaLAqfD4oU6+/9J53r6xgoZSHxVeG0vL3PSE4qiqTko3KHXbCEaT/PiVFm5YUcq62oIx5UmoGl2BOOVeG1Y5O4bn1DzJMttFqC6wcK4/iYrZkIYEjERgyyIfK8s99Ibj1BS4ONYVRDd0BsJJnFaZlG5Q5LTgcyokkipxVafIaUOWRdZUeLEoEk8c7eB4V5hV5S4OdYZ49xU1rK0vxDDAbZOxSCKlHhuD0SSlbhu9oQSyBLoB1QUOIrG5azeXCz4L7P/iG9d1bSpMJlntFCAygVdwmQ1649BY7uLb77uC7zx3mkcOtVPssHJXUx1Lyt0094b4t6dP85a1Fdy6tpKOQBxZENi+uIAnT/SzocbHqiovg5Ek0ZQ2LIISiCX5/q5zAPzxdUuQZRldNzjTEyZlGDSUugjEkpzsCtNUX4hFFukLJ9B0gzJPdrygrhu0DcYoclmGRQr6wwlSmkG514ZhmMe9DgWPzQwq7vRHqb9/J/UFMs9+6pas+01FYjqX2+RcSmAD/H+PH2dv8yAPfnQboxnvWT/4wU7+7mju+03E0P1+/GbfgpHXXYiS1QW6wdceP843nzkz5evftAw+ctN23v7vu7HJUFXo5GRPZPj4kDQ8TBzLVOJUiEZSREgL5BTYaB6MjzlPBM4+cAf/+Lsj/HxvO9+9dzPr630AqKrOnuZBFpU4KfPY+MnL53n2ZC+PH+3Jukeu+u4KxJElgWLXOJLwc9BuPvCdZ3jubJRSB7z6dzO7x/6WAb636zx/ev1SGso9M7pHLKnRE4pT5bMjz1DmOJxQGQgnqS6wL5h3aqEy1fmNDFxR6wVJpLbAQaXPhsumUOS0EIylqCtyUlvkQJZEKn12UppOXyhJsdvC8c4gpR4b1QXTE0CaaN46n8wopkcQhGcmOcUwDOP6mRVpLNaKBqPiA1+fq9vNKQKmwkxS09EM8+8711USSWn8/mj38HmFToXFxS4+ds1ijnQEOd4ZpMRto8Jr48FXm+kJJrAqEr/9kyupL85WRfrZay10+OOUeWy8Z0tt1jFrRQMLtW5mSuZgNpoSlwVZMhWs6oocOKwyi4qcDESTOBSJ410h1lR5ua6xhO1bt1x2dTMXWAVwP/H3+ZiecbjQ75Q1I18VgEeBiAYOxVRr0wG3Vebzb1nF8a4Qmm5w7fISNtQW8JZv7uJQWwAE2LKokJ9+dBtPHevihy+1kNJ1bltVznMnewnGVVZXeblvez2/fL0Nw4A71lawrMw9/NzfH+niSEcQp1Xivu2L6AsneGhPG7phcMuqcgYiSV47P4BFFvnA9npcVjmrbj5z4yI+euNKYOzgO14unrk4NtE1//i7o3z7edModFklDn/h1lmXYzxGX9P5v3+e1W4WgmT1fL3Ho8vR/6O/pOh9/zwvZZkNQ/X3L0+c4OWzA9gtErevKuFTDx8dd4zKrPPjXUEePdSFIMDdTTU5FVxH9zcz+c4y6/uhd5SyadOmad+j4TO/I6UbWCSBk/9w++QXjELTDb6/+zzBWIqGMhdvWjt9Gbl4SuP7u88TS2qsq/Fy23U75v2dWqjMdpwa0ugTAatForbQwbpqH5sWFeCPpgjFVc72hukJJXBaZf7+zSupK3JO+f4/fbWFzrTR8+7NtZNfMIfMeUyPYRjXza5Ilw8GkNIN0jk4MYD+aAJ/NJV1XlLVSWo6Z3oiGAbEUhrxlEZfOEk4nQcopeq0DETHGD0DEfNeg9Fs3frLlYnM8ISqYRgQQR2uw0VFTvpCCco8NuIpDU03hussz1gSBrgnOWeiFaT8wDM71FHCbwkNDANU3UDVdGyKSCyl0RGIoaVzcQ31J/3hBAYgGNAdMJP59kdSxNPvRVcgTihtUfWGEgRiKYbWtQYi2f3HUH8STWokVI1ALIVuDD0vyUD6eFLViSbUMfK6z57q46M3zk2dzBWvtw4O/x5Pja+wl+fioi3gnHJToSuYAMydjH3NgSnnDRpMj0OGYb5TuYyeueZHJ5PMwOZBS7/76gy/q5SmE4qn5yqRmc1V4imNWDof2GB+DL+gGJiGjw5omk4kac6p+kJJ4ulBqidktvt4SmMwmqKuaOr3H0yPWaPHnflmLpKTuoC/A67HzDn5KPCAYRhj948vQWyyQJnXRrc/xlCO0iVFNvoiKWIpnRK3lYYyFylVY39rkNoCO3duqKbMY+OvH9pPZyBBgV3iqmVlNJa7uW9bHS+fH6C6wI5FEllR6aHEqfDTPW1srPNx9bLSMWW4bXU5RzqCrKyc2ZbzfGGTRXRAQSemMkY+WsBcZRAEMASQBbDIEmUeKw5F4EhHhMxpi8cmcevqCgwDyr02ilwWFhU7Ccc1rllWQmcgRmO5mwKnhW1LpvF2vsH49j1r+NyT812KNwbv21LNT15pG27HXpvE2io3rzX7iatmzq/b1lZyvCtIgcPC+hovr5wbZH21j7dtqOJIR5BQXGVzWor607c38uVHjiMK8PdvNmVGb2gsJRRPkUjpvGV9JY0VHva3+XnLukpWVHjojyRRNZ0Ntb6ssl3XWMqe84PUFjpw2xSWlcn0hRMkUjob6wqIpzQskkCJ20qpZ6yU/oN/dPXw73MhlXxbzfjHpipZ/bOPXcnqv3uMuKrxqYzEpABrK6wc7DQH8XdvLJnSsyZiMinu+WChSlaXe21UFdhpHYxN6z6lbome0MUzXs8/cAcNf7uTlAZLS0YMlI/sWMTP9rSyvMzNXU017Do7kPOzjP7eN9T6iCZVFEmkcQouY3PRbr7x3itndN2NjaXsPjvA9cvHfzcmwqZI3LKqnHN9kTF9zVTxOSxc31hKhz823OcNkV9smxtkATx2GYfFzAWn6wY1RQ421RVS5rWzZXEhgViK5v4o2w8AybgAAQAASURBVJYU8tyJPmqL7Kya5vxzoc5b50Ky+kGgC/gPzHnsRwCPYRgfnX3xTPKS1eOTly0cn3zdjM9kktUTcbkPPvl2Mz75uhmffN2MT75uxidfN+OTr5vxydfN+FxoyerNhmEszfj7k4IgTJ4FLE+ePHny5MmTJ0+ePHkuAjOT18gmLghC2dAfgiCM9c/KkydPnjx58uTJkydPnnliLnZ6Pg/sFgThV5hhG+8AvjAH982TJ0+ePHny5MmTJ0+eWTMXRs9a4N1AU/p+9wGb5+C+w0yWN+NCIjCicJHJp25dzt7mQSJJlWsaStmyuJDdZ/rxR5Mc6wzS3Beh1W9qOYiAwypx5/oqPn37iuGcGEMcbvfz78+cQZYEPnFDA0tL3Xz1d8c42hnkTWsruGfT+HJ/81k302VItGBI6c5lkTAwiCSza7fcY8Fjk2kPJJAEAUEwVXPsioTbJmMAiiRyY2Mp79pSx+nuEF985Ah94RSLS5w88qc7kCXxkqqbi8mHt00QMZ5nTtuNnG7vQ5GTkgAVPhttGXlCMmOk3vWfu9nT7EfTDbx2hT++bgl/sGMxoigwGd3BOL/Z34GqGxxq9xNPaXzihgbW1Zi5vwYjSX61rx1BgLdtqKJtMMZzJ3upLXRwx5qKKT1jvLwZcyFLLQFnZnBd5v93n+rlPd99FYAN1V5+9Sc7ho/d/o3nONoZBuCOVaX8+70jEleZ9/vybct43zUNTMbOA2388U8OAGbf1p5RNyXAawtALnqhSFb3dARn9E75bBL++MUTMviDzRX8+kg/gViKK+p8/OxjZkrCL//2CL/Y147TInPLyiJ+9Eo7SW1sPPR7myr5h7s2EE9p/GpfO8FYitvXVFBTOH6Ok9H9zWwlq2f6nX/z6VO8cm6AqxuK+cjVS2Z0j9kSS2p85Ad7aBuM8p7NtXNSN5cLum7w24MdtA3GuG556YzHKUWEpaUublhZjtem8OZ1lZR7xwrVzIQDrX7+/ZnT2BSJv7ppGXXFU5O47gmZY5dFFnnbhirc6bxwF4K5cG97r2EYrxqG8S3DMP7VMIwXgI/NwX0XBAZjDR6ADn+ME10hgjGVXad7OdDqJ6nq7DrdRyiu0h4YmdToQDyl88q5fpr7o2Pu9eLpfgYiSXqCCV4+O8BAOMn+9P2ePdF7wT7bxUZnxOABCCe1MQYPQH84RUt/lHhSI5xQCcZUUppBKKHSG04wEEnSH05wqjfMoTY/z5/qozecRNV1mvsjnOgKXbwPdQny4z1t812ENwyqkS3BrhnQk5a/HeKHL54yz1VVDrYHUXUDA4gkVJ481k0wPjXp1mOdQcIJlYNtflr6o0QSGs9k9B+nesIEYin80RSne8IcbAuQVHVO94QJxdUJ7pybuV5QmOrUdqLnPvDY8eHfD7YHso4NGTwAjx7JTiqZyecePTmlcnzqFweHfx/di81Xr71QF3m0GQomXUyDB+C7r3YyGE2iGwYHWkfaz5PHe9A0nd5QnOdO9Oc0eAB+vKcDgHZ/jK5AnGhS42hn8KKUfYgf7jo9o+tePN1PUtV5/lTfHJdo6hxs93OuL0xK07PyHOaBQCzF2d4ISVXnYJt/xvdJ6dAyEONg6yDhhMrxrrlrny+c6iUQS9EdjPPq+YEpX3eyyxyD+sNJzvVFJr9gFsyF0RMWBKF+6A9BECoYyXt0WZDrw5R57CwuceK0SGxeVMSaai+SKLBlUSEOiym7nHm9RRbZWFtAbdHYFZ8tiwvx2BQKnRY21Rfic8isrjLvd+VlJr2cuZjsUEQclrFN0OdUqPTZscgSDouEyyoiSwIOi0Sh04LXruBzWFhU5GB1lZdtS4oocChIokCVz86y8smy0LyxuWvd9JPG5ZkZkpDdf4gCFLksWefce6W5qyDLMo3lbqT0S2K3SFy9rATPFFe9lpW5sSkSKys8VHht2BSJqxpG5GeXlDhxWWXcNpnFJS5WVXqQRIFFxU7ctulv+n9m8bQvmRMmWu398xtGdmhWjOoHGjIkiK9ZNn6/+qXblk2pHJ+7o3H499FjxMxEf2fPQl0Jl4SZTQlcOcaHC8k960rw2hQEQWBFxYjU7tXLihFFgUKnhW0Nhcjj7IreucYMaa7y2Sl2W7HIIiumIFU9l9y7Y+nkJ+Vg86ICJFFg6+L5m3OsrfJRU+BAEgWua5yvt2hh4rUr1BWZdbOq0jvj+8gCVHhtrKzwYlOkrITVs2XbkiJcVpkip4Ur6gqmfF1DmQuHRUp/xqknQJ0JcyFZ/SbgW8AvABW4C/iSYRj/M/vimeQlq8cnL1s4Pvm6GZ+8ZPX45NvN+OTrZnzydTM++boZn3zdjE++bsYnXzfjc0Elqw3DeEQQhEPArYANeKthGIdme988efLkyZMnT548efLkmQvmQsgAwzCagf+ci3vlyZMnT548efLkyZMnz1wyJ0ZPnjx58kzGeK50l7u7XJ48efLkyZNn/rkkjJ5Of2zOVGlcCoSnJoYEwMMf307rQJQfv9TMgTY/LqvE3VfUcLY/Soc/SrHbyl0bq9l5sJNnT/biskhUFTi4aVUZe87189ypfkpdCi//7c1Z9+0KxNnfOkgsqfPzPc28dm6QMo+Vz791DUUuCxtrpxYEdqxzZlKgF5sSl0IkqVPiEGn3p1ABmwiGCIYuUOqxMhhJkNLhmmXFLC11U1Vg55GDHZzujrC4xM65vgixlEF9oZPFpU5WVXhJajqdgRjLy930hRKUe228fWMNdouUl6zOwcpyFw/90ZXzXYwFzWTtxiJCDtHBYZwKaIaAAPzNrY20DsR48ngP66o9FDgsGMC+5gGOdYdZUuTkex/azOH2ICsq3NQVOdnbPMhgJMnWdFDoaA63B+jwx9i8qBCfwxRFONsb5mR3mHU1Xiq89jHXTJVIQuWlM/0UOBWuqCukpT/K0c4gqyo91BQ6xpWshvFlc//u73bygyQ5j/3VT/bwywOmStOuP1lHdXX1pPeb7FjjZx8loer85U1L+NMbRsQG/urHr/HLQ6Zq21duX857rh4J+H7HN59nb1sIATg3DSN8yad3ohlw++qSCevmYrJQJH4zy6EF46z7/OOE4mpONdTx+PRty/jPZ8+iI+C1STRnSL1PlVXldo50xYb/tisi8ZSOgSlAkRnVfP6BO1j6mZ2oOlT5rHzpzjVsW1xMVyDGQ3vbONkV5HRvhG1LCtj9ajvNo56VWd+vnR8gGEuxbUkRDsv4U625aDcNn95JyhhbhunQ3B/hWGdo+F1fCCyUd2qh8K1nT3GwNchHr15MW39oxvMbRYBClwVJFGgs9+BzWpAEKPfauX55Ce2BOF2BOOtqfGyqLxy+bvR4sJA41hmkZSBK0yQCCvNi9AiCIAHfAVYATxuG8dmJzu+LJKmYo2dPx+AB+KufvkSJx8erzYMAJKIq//3iOSyyRDylYe2NcLonQsdgDB2IpXQGowHO90fwx0wZ2K5Qik8+tI+v3b1h+L6PH+nibG+Yg21+9qWlMdsDCf77+dNsXVJChdc2pcmLqs9OiOJi0Zuu+ObkiARpXCet9WrQ5h8ZzJ441kvLQJxgPEl3MIFumG1giMOdQZoHIrx6bgCbIpHSdHad6sOiSBS7LJR6bNy0svwifbJLi6NdYX788uihOs90mMjgAYikYGgq9dWdxxAkEU3XaRuIUuK2YmDQEzLb88neCP/97Bm8LitnesPcub6S50+agseqbnDr6ux2PBhJ8kRayjWSVHnbhmo03WDnwU5U3aDDH+NDOxbN+LO9eLqPIx2mhGmZx8YjhzpIpHTO90f4w2uyc3ds/MxOXv+KORGZaPDNNHhGM2TwAOz45gHOP1Cd87z6+3cOT3ometYd33ieuGp+Qf/8xJkso2fI4AH4zO9OZBk9e9tMmXsDeMs3nuM3n7hm/EKnue3rzw5L8P/ucLZIdWZ5LyYLZZFndDl6QwmkGciif+Op00TTL5w/Ns3BO02mwQPmGD3E6NEzs9zt/gQHWgMIgsDvD3dxts8cc0SgbTBKrk8z9L0390fYlSH9fMOKsimVdabtJpXxQX744lnuvXL60oqPHOwkqZppHz52zfzk6ZmI+XqnFgonuoI8+EorhmHw1UePMRjXZzwvThnQnR6DugK9uGwShiFQ7rVxoM2PxyrTG07SFYhT4rJSn863MzQenOuL8EfXLpw2Ek6oPH6kC8OAQHTifuLi6kGOcBvQZhjGdmCzIAhV81SOSVnic+CxyVlSyzZZRExL0UqCgMcmD8vMgpmA06FIWTKmjWXZspUeu4xFFnFY5KzzitxWFEmYcGXockcEZEnAZZERx5E6VSQRp0VCkQUEQcBlk5FFAYskUuC05Lwmj0lVwdwkIsszOXaLgCKZbVgSzXYti9ndbpXPlLf32GScVnlYDtdjH9sH2BQJqyKmzzelrEUBXGnJaY99dkndhq6XRQGnRR5+Ri7Z7K0Nc9tHKVNUNS6c4NiKctfI/aSZySSvrp6axPCaipnLxr7REGYoWe1QpDkuycRU5OgaPTaFIpf5jg4l8JWl3FOnobfEaR2ZE3hn+U5Ol+vqZjb+edJ9yMUub56pUeBQsKTbXbHLOsnZU0MARBFkUUSRBJS0LLtNkRBFAZsiDY8tMNI2co1N84kiCdjTfcVkZZuvkl8JDC2pPAusB9ozTxAE4aPARwGqqqefQV7I+Jm5MPvf717FX/3qOGuqPOw6M5h1/rISB6GkitOic6pXZXGBhX9673b80SRvXjvIL/e1UV/g4D3bFnGmN0xXIIbbpnBdYynHOvx8b1czjRUuChxWrlpewmAwyhd3nuAtGyr58KgMx3esqWRttQ+LJNLcE+SBx09yx9pybltThdeuTLnjqfXZmdn6V25kGF7BEoFNNU4OdkZwKNCfsWCmAFUFFhaVuNjfPIgkGERSYGimy1qJR6HIYaM9EGNRoY1tS4o51hVhy+JCnjraQ1coxtqqAlKGRn8oyc0rS9nfGmAgmuQPr16CKEmUe60caA3wytk+blxezItnBhmIJtm2pBiPXWFxsQtV1+kPJ6kutDMYTeKxKSxP50UodU9vwBQAtxXiKbBIEEtNnjBxtHvE6Dq0CiPtTzfGv58IOCSIaWPPcUmQNEDQYSitZZVHQdV1esMaYvr6XAvqErCyzEl3JIlFFvnT6xq4fU0lfz/J57qYTLQ6vdBW9iwCvH1jOc8c76U7kvvb/Oi2Gk73RVEUmc++qZGBSIpf7WvnlpVlqLrZXvyhKN96/jwfvqqeN62rpt0fo8pnx6ZIvHdrHaF4iv+fvf8Oj+Q677Thu0LnBDRyDoPJeTjkDHMmJYoSFS1ZOdiSbDmt83pfv+v1ruN6P9m7XtnWa1uybGUqUBKVSIpiJoeTc0LOQOdYXfH7oxo9aIQZAIMhMCTu65prAHRXdfWpqlPnOed5fr/WOdIHPE6JD+xrI5ZVaSu+LggC772xhdGkQkvl1aUc7OsI0xByE3A7qPQ5efcNl44NwD3tlvrcxx4s/dz3l2+5bM3WfCkqfX/5Fjr+8HE8Djj93+c/19O3O3yZz/qb9+7BMA9xYjjFdz9944KP46e/tZ+3/Z+XuaE9xJ+/azcL4a/fu5vJTIGjQ0me/s191P3b3Mf7WnK587CSx7G+zs/+zbUIlsnL/QmS+UvrJI0BmeaqAAf64mX7ePMGkT96+2189olz1PqcqCb864t9pdc31vk5N24bzv7hmzfylz86N+s4HAJ8+ZP7+ZcnDvGTHvtJeXNnGKdg0R3Ns67SSV7TOTqao6XCw1O/dw+//bWD/OjkBH/17u3saqmitcrLzqYQL/VGMU2Tp89N8siuBiq8bu7+m59f+izgQvG8V/tdfGBfK9mCMac33+XabSn86u2tfO65AW5uD5WliC6G9+xtKbvXVxur7VnwWlMb9PAPH9zD0YEEb9vVxIHPBokuYvupsUpjQGJ9XZCdLRVkCha3rKvCEkAWBHxumS0NQSYzKllFp77CXRZgvWtP86q8RlyyxPv3tTKZLlzR5+eqfXqWgiAInwf+zrKsU4Ig/BoQsyzrK/O9f82nZ37WtNrnZ61t5mclfHquF0+gtetmftbaZn7W2mZ+1tpmftbaZn7W2mZ+1tpmfi7n07NS6W1pYMoGNgiLCljXWGONNdZYY4011lhjjTUWzEoFPYeAe4s/3wK8ukLHscYaa6yxxhprrLHGGmu8zlmpmp5vAV8WBOE54BuWZcUu9+ZkTl2WPGUZ+J17Wvm/zw9iWBZXEoNpCcm8c28bEvAfr/QzkbHzkKuAt97cxhdf6kcAfuOedXgdAn/9k4vsaPJz47oa3rengf/ne2e4MJ7mf71zF2ciGVTdYFNDEEUzkEWBDfVB6oNuHJLIS90ROmv9i87JH0vkVkUO90xckl2jktHt/wVJQDMsttQHcDsknutJAPY58cqQ0mF9tYeBWJ72ai+3r6/h2FCSmoCL8VSWSErjLdvqGEgU8LkkxlMFgh4Ht3bVsL+zktNjGUQgXdC5Z2MNYb+bRFZZlW2zkohA9188dM32/3po7+WSOvc5BO7eVM1L3ZNEc+ABPG4BQZTQ8zpJC4IO+NN37ODcWIZav5OLk2mSioFDFGgKe/jknV0kshppRePCeAq/x4FDEqn2OXE7Zap8Tiq8TgZjWb51eJjJVJ6MovHAtgYCRfGB3kiWDXVBbl5XxVA8x3A8z6b6ID6XxNmxNIZlsbk+iFMWmUgpFHRzXjnSpUhWT39trnTFq9nfXK999/AgZ8bS/O7963E4HHNuN3Obt/3R4xw3597f5fjUFw/wan+c735m/6qR112NktVVlsXPz03w0S8sbn5zqjbIATy8u5HvHBmZ833/8Ymb+OC/HJjztXfubuTb82w3k6d+507++vFTPHMxyj99aA93bqxnMJZDALonM0ykFL59ZJiAS6I3kuPCZHbW8c7kYJ+tMrqt6ZLwxWA0R08kw63rqpflunnX/3mCQ8MqQQmO/9nS9vHbXz3Id4+N87Gbm/njR3YuaR+Zgs54SqE17MUxj9jDYlgt99RqIZnXiGbsuhXTsq76OeUAZNEWw2mq8uCRJCayBUIeJ7IEb93eyE3rauio9uGQBH5wfJSOai+SIDKeytMfy/OuPc0Ypsmjh4bZ3VrBulo/DSEPfZEsI8k8tX43Ia+DvGrgdUnLJsIAkFI0u44n7J1XXGQmK1LTs1hcDeutho/87UofxqKQKC9Id0gChmnhcYiYFrhliZ0tIW7fUEs8U+DV/jguh8jf/sJuwv6Fq6+4GtZzvbXNcuIQoS7kQdMNknkdv1tme1OIv3//HsLtm9/QbTMfQRc4H/+v16SmZ7mDnpV40K3EPSUKttBF2d+wlcT2d1Tzw5Oj5BQdzTTxuuxgZ12tn031QT5wUzPv+IeX6I/mSvuQBGgIOYnn7F4o6HHwS7d3cGwwyXhKYXtTiC0NAb5zdATTsnj7riZu6armmwcHsSy4f0td2SBtiultUwO8Oo+M9PTzttyvXW6bx4+P8NvfOIZlWexoruDRX7nlqo9jPv7Lo8f48sGh0u+j//ZbZdfNapCsXqmB4szj0L71Bzje9VcrcixXww9+/TaeOD3Oi91RNMPgaNFe4nJMb/MfHBvh31/uRxDgt+/fwE0dVUQyCr/9jWMUNJP9nWH+8MMPX/V1M729H313HXv3zlnOsOB9LOUYdMPkiy/2kVZ0Omt8PLLr6kV5Z/bFb+TAR9EMvvBCH4pmsKUxyNvuu/01eU6tr/Xx4NYGTo0kOTuaIqcZNITc9EZySAK0V/vIqwbj6QIC8MH9rdy2voZ/eb6XiZRC0O2grdpLwOUg4Hbw/n2t1ASuPvAp6HZ75FWDzQ0B3rTtkoD3aqzped0zU9fJsixMC1TdxLQsTMsirehkC3rJg6agmaQLy6nF9vrHMCFX0DFMMEwL07TbVdGupLv2xiVduPJ71nhtmWvuyQJiGQ1FM1B1E8OyMEwL3bDIqQYFzUTVTZKKQbZglO3DBFTDtO8Jy0IzTEbieQq6fV+kCzrRrIpmmOiGRTynkla00j5SypX7ockrvuO1p2cyw9REXix7GYOgZeDE6JUHwGvY6OZiLElXD1P3QV7VMZbgiTdR7GwtCyZS9s/pvE6h6BUUzSz/Nfrl89f2up8P3bTIFuz+JbUET6Y1Lo9qmKX+O7VEz6qloGgm2YJOPKdiArphkVftcaxVPJaMqmMVn08FzWQ8pVDQ7OdPXjNQVBPVsLfJFJbn2tAMqzTOS+UXvs/VJbb9GuARIb+I/nd9nZ9cQWM4UT5S9DpgygOpxm1LDU8Zn66r8XHn+iq+9NIAugW7m/2YgkQ6r3NDa5BY3sAti9y/pZ7tzSFkUeDrrw6ysT5wRbm9Nwo1PifxvIoEaKY9iKv0yOiWhalbGNi+J9uagrx5WwMnh9NkChoI8P4b20q+CmvM5vu/egsffXLp278eUtheK5zilc1Mb11XxVgyj0MUmcgU0HQDC6jyu/ird2wnb1iE/U7Oj6ZwOSR8bom2Kh81fjcd1T7aqnz8zv0b+OfneoikCxjArpYQG+oCxHMqIwmFzQ1Bfu2e9bzSG+XcWIY7N1TTEvbilCV00+ThHY3U+F3EOlVU3eSGK7haQ/ms61KlkjvDIj0xu4G+897Gq/6sX793A6/2xRlN5vnLd+4oe+0tG8M8fs7OpP5/H5jfWG+hs8nf+/U76Pqjx9FN+IXdjfzdmmT1vMdR43fxoTvX8Q/PdC9qP/duCPPUefuc+UXIXOPY6Z8+dAOf+Y9D6BZ0VXu4oa0SVTdpr/JybjyNYZr0RXKouok+Rww087z/wt4WMgUdj0PkTVtto+GOGj+/eFMrFyYyvG9vC//zMtsvhc++/5Yrv2kOZAF0y05NXwpuh8RD2+vpiWTZ3VKxtJ1chjfyKg/YflEPbKlnKJ5jb3uYa+1gJQId1R7evruFOzfW8PDOev75uT6qfE7qgm6OD8WJZjU+fmsHOVXnqwcGWFfj574t9dzYXomAwMXJDK1hL80VHlTDIOhx0r4ICffL4XfJPLjVTj9dyPNqiusivW1Nsnp+1mQL52etbebnaiSrX0vWJKtXF2ttMz9rbTM/a20zP2ttMz9rbTM/a20zP2vpbWusscYaa6yxxhprrLHGG5a1oGeNNdZYY4011lhjjTXWeF2zFvSsscYaa6yxxhprrLHGGq9rrgshg+XyzVhJNtX7GUvkQRTwyhKqafHA5jp+8/4N3PnXP0MpVkX+yVs38tFbuxhJ5Hns6Ahuh8i7b2gueW7M5PXQNldDpVdmfW2A0yNJMqqJIMDe9kq++kv73/BtczmqV/oAFsjlzt+1qvdZTdeNAIii/b8kChiGVSqgdkogCgKGBZIgIAmg6CYItv71TP1CSQCnJGBZoJkWTlnE55RQdBOvQ+T+LQ20VnnRDQuXQySvGtyzuZatjZekq2e2zUJkpK/1a9P//hffP84/vTBof1+gexk+az7u/JPH6Vcu/T46T9u81qy2Y4DZbXO9cM/GGl7ojlLQF66gEPY5cEkSqmEScMv85r3recee5tLrmbzKx/7tIOMphfff1DrvPbUYluOc3/KXTzGeKtBc4eGZ37970dsbhsEnvnSIixMZ3ry1nv/y8JZF70PVTf74sZMMxnK898aWZWmb1wuxjMqdf/MzcqrJTe3ha/KckgT7f8MChyiwsznIQFzBtCxqfE56ozksLDbWBfjtBzZy18ZaTNPk9791nFd742xvDnHnhhpGEgo3r6uiJezhX57rpXsyw23rq/ng/jZc8tIkGAzT4rtHhhlO5LlnU+2clgoL4boIel4P9ExmEQVsU9SCgVMWOTmS4sJ4phTwAPzDz3v46K1dXJjIoGgGimYwEMuVDTzWuEQip3NhIkO2KJFlWXB2JMVYSrnClmtc76xEQPRaY2HLsgOYpsX0oZdq2O8QAK34vwVz619jP8jy0/oaRbPlqk0ssCyODMYRRNtDLFI0wDs9klp1fc/lzvu/vjRY+vlai9b3r8Iu5noMLFYzJ0aS6MbiJOMSOQ2/y5aVdztEfnJqvCzoOTqcZCSRB+Bn55Zf+L39Dx9fUv83nipgWRbDxWNbLGMplQvjaQCe744saR990Sx9Edvw9fkLS9vH65UnzoyVJMGPDSWuyWcY0x4dmmlxdjyDZYFpWeQKOmox+O+L5jg1kuKujbWMphRODqfQTZNzYyn8Lpm6oJuTw0nymsFIIk9ONeieyDCeLNC6RPW2ZF5jIJYD4NRIcslBz1p622vEpvoAPqdE0O2gNujG65TZ01bJxvoAPtel0/Db93eV3h9wy1QHXLSvyVjPS43PwZaGIAGXZM+KC7C7tZKGkGelD23Vcq2lLlcD7X/4+Jz/rjdEbMdspwQuh4hzWo/tkQV8DhGnJOB1igRcErIIsijgmKNnl0XwOUS8DgFZBK9TpMrnwO+SCbhl9nWE6aj2Uxt0sae1EpdDZEdzxbzHtpKyzPPxBw92lX52XuMLfXvVtd3/Uni9BPurhRtaK3DKIsIitqn2Owl6ZMI+J26HxMM762fsM0x7lQ+HLPLQtvp59rJ0lnoNNFd4EAVhyZLCTZUetjdV4JBE7tlUu6R9dFb52FgfwOUQuWfz0vbxeuUtW+oJuR2IgsBN7QuXaF4MDlFAFu2r3SkL7GwKEfI4CHkcdNX68ThFXLLIhlo/u4qy5A1BNze0VeKSJXY0VbC/swqnLLKrtYLN9QHaq32EvA42NwSpD7mXfGwVHgedNb4rPpeuxHUhWV1dXW21t7eXfs8UbKOwgFtGFBbTHb3+6OvrY3rbgD3Rm1Y0BAH8bseiOuzXE319fTQ2t5Zm3NyON8Jwf2HMdd2sBlbDvb1a22Y1cLm2yasGBcPE55RwSG+8+bTVct0omomiGXidEk55dZyH1dI2y4kFZIqGvgG3g6V2V729fVTWN+GSRDzXOlK/zphqG6ck4l1rmzKm7indtA0/19roEocOHbIsy5qz87su0tva29tLeuQXJzJ8+eV+cqq91Hbr+hrevqsR+Q34kIW5tdqfPjvBcxcmCbgd3L2pthSRryZM0+L7x0cYiOa4a2Mt25uXP4Vm7969/MrffpNUXkMUBD5z97rSdTKRVhAFgerXkYnpRFrhu0eGkUWRd93QTMgzdx0YXJ3G/8G+GC92R1lf6+fN2xsWta1hWowk8lT7XbMe8D2TGR47OgLAjuYQ926uW9LxXS1r/gfzM1/b5Ao6v/voMeJZle3NFfzRQ5uv+rNM0061qfI78TpX/6NqNVw3hmnxt0+eJ6Po1IfcfOrO+U1YryW6YfLtI8NMpBTu31LPBx6+e1naRtEMJtMF6kPuFQ+sX7wY4R+f6ca0LD50czsPbl3aqk3zhm1s/9XPUe138jfv2UnI61zmI71+aSm2TZXfyd+8eycVvrW2mWKqv/nCC72MJPJ4nTIfvaWdymIbJXIq3zo8jGlavGNP0+tqrHMlBEE4PN9r112kcHQwztmxNEcGEsiyyGAsRyynrvRhrRpiWZVnz09yaiTFeEqhapV2EilFo2cyi25anBhOXrPPqfbb37/S50AqLttenEjz5ZcH+I+X+xks5oi+Hrg4niFbMEjmtVJe9LXg+FASw7Q4O5ZG0RZXOfHTU2M8emiIL7/SPytXvsLrxFGspHwjddCvBzTDJKPomBak8tqy7PPJM+P2tfLyQCmXfI3LI4kCvZEsp0dTK9q3RbMqw/E8mmFxamT5+vdvHBzk0UND/OD4yLLtc6mkCxqqbqIZFill6de8ohpMpBW6J7Pk1WtdiXZ9kZ9qm4kMqcLy9CuvJ3ojWY4OJjg+lKSgG2UTid2TWVJ5jUxB5+JEZgWPcnWx+qfPANOCbx8eQjct0orGzpYQE2mFpgoPbVVeKjxOMgUdv+u6+DrXhGfOTzIQy9FR5SXocbCjOcSWhiAt4cvn5+qGiaKbr3nbBd0O1tX6GYzl2HENVnmmeMv2BsZSCjUBF6phYpoQy9qdp2VBPKdesY2uF9bXBTg9mkIWBYIema8eGCDglnlwa/2sWVFFM/j3l/rYWB/kpo7woj5nZ0sFL/dEWVfjX3TKYDRrT1BkCjqaYTFdyCXsc/Khm9tRNIO64OJzfzXDRDPMZVsVmK8GaK1uYjZBj4N37mnmzGiKB5epTuHcWJrD/THCPicF3Vg1qVqrGcO0cMkChmnhlFYusbnK56Q17GUspbB9RsGxYVo8cXqMaFblvs11C77XDdMikbP77mjm6iY6dcOkoJv4ruK5t7+zmu6JDJphcffGpdefOIrXdcgj43WtpSdNZ+qeD3rkeRVs38jEsip1ASfjqTxglWnYdNX4OT6UwLRgfa1/xY5xtXFdRAmKZtAftWetNtQFqAu6aa9qZFtTCN0w+eqBASIZlf2dVdy87uqqS3sjWXTDZH1dYDkO/TUhmdM43B8HbMnBfR1hUorGbetrLrtdQTf4yisDJHIad26sYU/rtSmOmwtRFHjbzsYrvm8kkSeR09hYHyit1CwGWRJprvQSz6p87dVBVN3kwW11bG8KIUkCWxqCSzn8q2YoniOt6GysCyAu4XvNRU3AxS/d3gnAE6fHGUsqjCVhU32Wrtry6zlT0IlkVCIXI+xprVhUeuiWhiBOSaS5cvFiEfdtruNgf4z2Kt+c+etTRZOLJVPQ+eorA2RVnQe31rN5hc7rGxVBEHj77ibevrtpztcHYzmyqs6G2oVf78OJHKNJhbxmz6avdiwLTgwlaar0EF7BFfaxZIFETmU0VVixY5AlO8V2LkYSec6M2ipfB/vivGWHnSKbKej0TmZprfLO2QdIosCbt9VzbjzNzqsoZFY0+7mXzGtXlf4d8jj41bvXL/k4prAsW3reAtYWNMu51DYChrnWODPZ3hTic09fZCxZoNuV5dx4unQ9h7wOPnZrx8oe4Crkugh6nLKIUxaJ51QimQI3dYSpDbj4+quDDMSyTKQKNFZ46I9mryromV5TcN9m85rUmVwLzk+kGUsp+FwSY0N5BmJ5fvn2jiuu3iRyWmnmrD+afU2DnoUQzRT45sEhTMsikilwx4bLB3GXYyyloGgGqbzGY0dGePvuJjbWX5vANpnTeKE7QpXPyb7O2dfjRErh0UNDpZWmW9Yt3jVH0QyeuxBBlgRu76qeFbS0V3k5PZLC4xSpnWMmVRQEzo+n2doYnLWtYVo8fzGCohncsb5mVnDyg+MjDMXzeJwSv3Rbx6ICJotrM3idTBfIFHTAvpbXgp7XnpPDSfqiWW5oK1dPHE3m+dZh+3pPrtPmvCfmwueSqfK78BWVGafzo5OjvNIT46Ht9dzUsTpk1JJ5lSfPjON2SPzS7R0rVnMSzapMpAsrGnjNx9mxFCeHkxzuj5FVjbJV/seODjORKhBwy6XJm5msrwtc9YRkPKeSzF967q10zaui2SlcGUWnsMh04dc7U22TVjQUdS3omclIIk9vJEskXUA3LE4OJajyOV832SvXgusiX0AWBX7p9g7qg25iWZWfnhrnqTPjPH12goN9cTKKhtshsn+BD9P5mG5AphrXR+djmBbPX4hQG3ChqCZ9kRxHBuJ87dXBK25bG3CxvSlEbdC1agYO09EMC7O4Xnu1Of2dNT46a3xMZgqYlsWPT44t2n9hoTx/McK5sTQvdkcZTc72PFANs7QMvdTvdWQgwcnhJEcHEpwdS896fX1dgE/e0cnHbu0gOEdagGlZtFf50A1rVjucH09zuD/O6ZEUh4oriDOPH+wUEXORMcxTZya4MJ7hyTPjy5q/3hr2sqk+QH3IzZ621RW8vxHIqwZPnhnnwniGp85MlL2m6tOu90Xccx/c3879W+r48M2XinMBFFXnSy/2c2Y0xb8817ssx78cTN0LumFiLPbGWCZU3SSWtVd4xuboe1YSzTD5yclxXumO0R/NIQoCL/dES68XNPvasPvHa9d+dQE324rPvRvbF5faey3Qi9dKXjeYzKxC86cVRCu2jaKbjK95781iJJknrxmIooCiG4wkFX56enylD2tVc12s9AC4ZIm6oJu0kiHkkakJuvE6JdIFgc0NIT58c/uc+bmJnMrJ4RTt1V6aKy8f/W6qD6BoBrppsavl+hg4iYKA1ymRUw021Pvpi2QxLIu2BUT6giBw35aFK2QlcxonR5K0hr2vyUxCfcjNm7bVE8+pV70K5ZIlHtnVhGXZKYzThQ2Wmyq/E8ZBEOD0SIpUXi9bVWqu9PLA1jpSeZ09bRVL/wzs8185z4zu5eRPRUFgLKXQWe2b1Q6VXieiIGBaVulzpvPQtgZOjiRpr/Itus6i2u9kMl0g4HaURAuWA0kUFq0kt8by4ZAEAm4Hqbw2SzylrcrH/VvqyBT0Rd3HHdW+OWf8nbJIpc9BNKPOuYo5H4ZpcWQgjiyJ7GwOISyzJHrI4+DG9jCtYe+KyeN7nBKVXieRjEpdcHV5lcmiQIXXQSIn4XHKSKJQ9kx+685Gzo6l6Kr1L/u5mY4oCty/iOfelVA0gyMDCSqKXiSLxS2LuGSJgFumpWLNk286HofdNn6XTHv1WtvMZEOtnxq/C49DJOx3IQnCqhWvWi1cN0EPQFetPahfV+PntvXVrK/1k1cN6kNuDvfHOT+eZl9nVbHTtAe6PzwxxnhK4ehgnE/ese6ygzRBENi9ylK8roQgwAf2tfLDk6NousWD2+oYT9mSnqZplfLnC7pRmn1caqH3j0+NMpJQONwf55fv6HxNHuzLkaakGbbzvMcp8fCOBkaTtrDBtXqw7u+soiXs5VBfjONDSY4PJan0Ogj7nGjF45jpcm+aFk+cGSeWVblnU+0Vi3s31AWo2O9AFsVZaSwvXoxwbjzNLeuq503hy6k6h/tjxLMqpmXXgk1RH3Lzize1oGgGrXMY41b6nNx+hXqx+bh/Sz1bG0NU+11zpsVpxVnyNU+l6wtZEmmt9HAopbCuZnbR7FLds+dCFEXec0MLL1yM8LZdV64LnOLoYJznii7vLlmc1bfkVB2XLC15MkQSBW5bv/hU1eXENC1qfA6imQKNlUs3ArwWCILAe29sYTyl0F7lYyiR583TRC9qAi5qAgvvV3KqzveP2enoD+9ovKIoQbag43Ys/fzOx4vdEY4N2gp1IY+DxorFBZsu2TYYrvU7cTmvi+Sb1wy7tEGgxu/EtfZMmMWxoST714UZjufZWB/ALUvkCjpfeaWfN29vwCmJVyXW8XrkumgN07L46akxDvTG8LlkfnZuAp9LIq3oyJJAld/JF17opS+a49HDQ2yuD7Ku1s8v7G0pzSbLksg1mthfcXKawXBcwbIsXuqJoOoWL3VHePcNLexqrUA3LH5+boLjQ0naq328dWfjkvKYpwJGSRJW3BR2PKUQy6qsr/VftqZkeoH7m7bVs6n+yop2y0FThYcLxWJcURDQTIt/e6mftKJx3+a6WYPA4USe0yMpAH5+boKw14luWmxtDJYCj+7JDKIg0FGc8aoNzB7UHB2I8zc/PYdTFplMF/j9N22a8/gyBZ3xlEI8p1JQdbzTUuDiWZV/eb4XRTP42K3ttFcvTvllMJYjrxmsn2PGVjNMUoqG3yXPWolKKxpfPTBAXjV5aHv9dSUm8kYnmVP5ix+dIZ7TODuW5osfv6ns9aF4jpw69zUxH2lF4+lzE2ypD9I17VrQDJOjgwl8LpkDvbEFu3M7pUvX28zJr0P9MZ49H6HK7+R9N7YuSSnOsuy6pqYKz7yrr9cazTB5pS+OZsITp8ZW5BimmEgpRDIqG+ou9R9uh0RBN3j8xCh5zcC04IP720p92pVI5jUGYzk6a3w8dyFSqsGt8Dov65PzSk+UF7uj1ARcvO/GlmXz9Xv+wiSnh+1+WxBYUh1XPKfhyGpEsxq9kxm2NlUsy7G9HojnNBwZjWhG48JYit1tK5+OuJrQTZODvXFGEnkGY1kqvE4m0ioVHpmjAwkaKjzc2B5e8cmY1cR1EfRMpgv8y/O9ZAs6XbV+MgWdf3ymm6F4nlBRKtUw7dUMAYhkCrSEvQwn8jy8o5GLExmaKj2vWwPTkMdBpdfBieEk/dEcybxGwO3gmfOTDMZzxLIqsiCSKeik8hq9kcySgp43b2vgwniGxgr3isrHJnMaX391EMO0GG0Jcc+m+VMVphe490VybKp/7Qrcb19fQ03ARaXXiWFaJf+S3kh2VtBT5XcScMuk8hoHemOcGU2haCY3doT59Xu6SOV1flIcxLx1Z8MsNTawg41vHBwknlPxOCScl7neLcvOJZdMZgWwh/vjHB1MAPD02Uk+dtvCg57hRJ5HDw0Vv381e2fkzP/45Bi9kSxuh8Qnbusou47GUwrZgl3n0xfNrQU91xHRbIGRpIJpWpwaLfdlGU3mS8Idt3ZVL1gi/X/99DxnRlM4ZZG//8XdJdNGhyTSVOlhOJ6nbY6VyPnY1hTE5RCRRYHOGatRPZO2r1U0Yxe51wQW7xOVzKs8cXplhQw03SgpgBX0lVO8Syl2H62bFsOJ8r4umdMZSxVQNJ2XuqP4XTIP72i44v1uWRbfPDhIumi8GvY6SrViV6oB6i36lk09DyqWwQD05+cm+Iefd2NZFndurOGRXU1Lum6M4rFbxeNcC3ouMZWdYgEXJtaCnpkc7ItzcTKDohkk8hoBt4IsirhkkUReo6HCQ28ksxb0TOO6CHqmakKDHpmuWh+TGZW+SJZYViWt6EQyBT5+Wzs/PT2GzyWjGSY/OzvOqZEk/+WhzdeNCttScckSH9zfxt/85CyJnIZhWgiWRW3AhUuWqPa7CHkdOBwCLZXeJRdvuh3SqmhLzbxUKDxV/DoXqm7y375/inNjaTbWB3jbrte25kMShVIam2FabG4IEMmo7G2vJFvQSSlaSeXK65T5yC3tpBWd//3keUzLXuGc8pMo6JeK/pV5vnNBN6n0OdlQG6Au5Oah7Q2kFG1OIQMB+74SBQFxxuCsPuSmwutAN6xFr4pNVx8qzCHSMGVmqhlmSaRiirYqH121ftKKzu7WikV97horS5XPRW3ARTqvz0pvm0gp/ODYCAXdJOSRFxz0DMdzDCfyeBzSLGGZt+5oYCCWmzOVbj4EQWDDPAPrfR1VKPokjSF3ydB4saQLOt88NEh9wM1Hbm5dkaDHKUm4ZJGCbuJ3r1w6kG5YpcH89L4LoLHCw9bGIKPJfMmEeK6+YiaWBadHU4wmFTbV+fndBzcRy2lYljXLniGWUfmT758iU9D49XvWc1NHmMdPjLKhNrAsAQ9ARrEn0wRBQBLFJUntA2XKhI2hNVPm6ZS1zRIsEl7vDMSydh26YeF2CHgcMm1hD3s7qthUH2QirVy1jcvrjesi6Kn2O3lgax09k1mevxBlPKXQEvayvtaPyyFR5XfSWePnLdsb2dYc4rtHhjnQE2c0ofDshUk6FvFgvF4ZTSg8fW4S07INqtwOiV0tFTRWeAi45ZJCzM7mimXzhVkpqv0uHtrewGS6cFkhgExBpy+SRdNN4lmV44Mp1tWszOqBJAq8aZsddOVUnS+91E9eNbipI8ytXfYsjEOy63M+dmsHzZVeFN1gf2cV62r8GKaFblqIgq1U9mJ3pGjOe2mmu6vWz/1b6rmty2AyrfCPz3bTXOnl47e2z3rQG5aFaUFe08kWVJzypVS5LY1B7t9cR1Y1uLVrcR1mZ42fezbVklMNbphDRe1N2+o5PpSkrWp2sbdDEllXY6/kzhWorbF6CXmdPLKrkWfPR/jl28u9IS5OZIhmVQzT4mQxFWgh7GytIKsa1AVdOOVLjyrLsnj00BCRjMq6Wv+C/L6uRGuVlw9VtV3VPhTNJK1oGIZJStHxul77a1iSRWoDTsbTBVorV67wO+xz8tD2BsZTCntaK/mTaa81VXrY21ZJz6STOzfUUBVwLsgvzQIqPA5M0yLgceJxSGwq1ix6Z/Qlr/bF6JnMYFgWT50d54bWMLphMRjPUdANXPLVB4QPba8npej0TGbIKBpfeqmfD+5rI+Rd3HmfvtAuCK/PbJSlMj0VVrw+xIZfU6p8LmRJQDctZFFAM00CHicfvbVjyUH4653rIujRi4XNaUVjMJYlktUQBOiqC7CjKcSWhhB/9+QFIpkCN3aE6azxkdMMsCxq/G+MmZP/9cR5IpmCPXsvCoQ8du3TJ27rAASeOmPLGMqiyPbmEBMphSfPjNEXydFR7ePeLXVz1oisVjbWB67osyNLAoZloegGec3g7FiKwVjlkmp6poQGImkFBAGXLPHA1rolDc4zBb0k1xzJzDYQbCuej+cvRMgWU/MkUSit0H3nyBB9kRySKPDx28r9mHa1VJAp6PzRtwYYTytkCzrJvDYr6LmUFgLSjAftmbEUT52dQDctWsKey6YPzkQzTIbieXKqzuaGAE65/HMrvM55/ZYGY7lSCp+iGVfly7TGa8tEKs/XDwxR0A0++8QF7t18qb5CFCxyqoFpWRQ0fcH7vL2rBtOEtiovgWnXuGFaxLIqmmEymV45A86ZWJZFXjXBAtcKpVKrqsFEWkU3LPpiuRU5hinW1fjnVLI7PZLkJ6fGsLBFUX73wY0A/ODYCE+cGeeWdVW898bWWfuTRIH9nVWcHUuzt72SQ/1xPv9sDwCfvKOTG6etIFb7nSiaiWqYhL3O0nWSVnQUzVyWoEcURd6/r5WfnBrj9EgKVTdJ5rVFBz3T1c0TinrVx/V6Yno2QHKtbcqwLBiM5shrJgKQVQ1cDpGTw0kO9Ea4f8uamulcXBdBT1rRebk7ytmxFIph4pRFexDYVsknbuvklZ4or/bF0E07ZeaeTbXcUcxhfCOs8gA4ZFu62LKgvdqHSxbwOWS+dWiYezfXcHjA9lvZv85+MBzoi/HTU+NcmMiwvi5A0OMsOWO/XnBJEndtqOGFi1GcU7MhS5BJTuRUDvXFOT6UIJZRSSkaXbUBjg8ml5QrWxtwc8u6KsbTBW6dZ+n5p6fGePb8JKYFtUEXmxsupRXKoj2gEgWQ5igKFwVorfKQ03QqvQ5q58gzFwUBAVtGdmbtz0g8TyxrP2CmcuEXSs9klvPjtm/QkYEEd2+qXfC208/NUs7TWFKxz02N/7pfzbzeKGgm6YKdWhuZEYgUdAuXLGKa1qIUE3e2VLBzjtpDWRJtMYOBBA+tMpnyKSNtZYV83qa0GiyYZej6WlLQDb52YJBYVp3VBxiGxUhSQdWNsomrRw8PkVcNvnNkmHftbkaeo270ga31PFAULPjX53s4M2avHJ4YTpQFPXUhN3dsqEYzTLY0huis9vFST5SWsHdZZ8AHojkagm40w6TC46QlfHUpWJq2cnVYq52r9ep7vZFVdeJ5FackYFngdUrkVZ1sweD/Pt3NpvrQmknpHFwXQY9LFonnNHt22hKoD7h4154mPnJLBy90R3j+YgTTsvC7ZJrDXiRRKNV8XCsvltXGH7xpI3//1EXGUgVi2QJ+t4OjQwnaqrycGk7RWVTHmZrhag17yakmIray0rU0g1spnA7RTjNxSvhcEjuaQ2VO8QtBM0z++ifniGVURMGiOezFIYuIgkDTVeQYX8mV3uOQ0E0Lr1NiIlVg87Sx3QNb62ir8lIfdM/pxeN1yrx/Xyuff7YHv0vmRyfHeOee5rL3WNjn27QsVMNk+hrf7tZKtjXFKGgmdy1ytaUu6MLlENF0a9EDgIaQh3ftaSZd0Ni8SMGJyXSBr786iGlZ7OsIc0vXWuHma0mF18mm+gDjqQL7OsrTGsNeZ7FGDQLuq3/kFDSDF7ujpBSdp89N8MGbry4tbbkQBQEsCxF7wmlFjkEUqfA6iOdUalcwyyGR00oTJz2TmbLXLEAWQEfAMe35vL7Wz/GhJB1VvjkDnpk0VnhKniQNM2Sip3zGDNOeBKoNunlkV9NVfqtyeiYzJfW4B7bWzbIhWAr+NXnhMqaP3kJrKc9luGQRSRTxOSUM0+5bIxkTt0NCQKAvml0LeubgurjDQh4H9UEnh/pjeBwi0VyBzz/bzRdf7KOr1kdt0K5bqfG7uLkjzNcODNAXzWKZFl94vpdP39XJyz0xUnlbtvhaXwiGafG9Y8OMpwrct7mOrtpru9rUM5lBlgT2doQ5M5JiMJYjmikwliowFM9yuD/ODW2VyKLIyz1RXJJIS9jLr9/bxZde7MPvlumezKIZJqIg8P1jIwzFc9zQVsn+zqpF+9l0T2Z44vQ4tQEXdUEXx4aSbGsMcceGGsaSCt8/NkxvJEd9yM09m2qX1cNjOpZlEfY5yQTdBD0yd2+qJVfQefbCJG1hD5sbK664j6fPTvDc+UlcssjejjC/fs96HJKIYVpz6t//5NQY3ZMZbllXXaaQ90pPlEMDcVoqvRwfStAXzfLmbQ28Y3dTWfsapsX3j42QUjT2dYapDbipD7n55+d68Dgl3rm7mVMjSf75uR5GkwoVXge7Wip4z94Wmiu9WJbFeKpAPKvRE8nilESq/U5Gk3kqvc5Sqolp2YMPzWSWqqFhWvRMZsiq+oIKjKdT4XXy8Vs75m2f40MJXrgYpaPax4Nb68q+u6IZvNgdIa3Y6kpNMwYylmUxllLKvscUBd1A0Q00wySvrcws+xuBnGrwuZ9fpKvGX5pxB/C7ZW5or+RQX4I7Z8zsRzMqeVXHBIbj5a7q4ymF7x8bweOUeMfupjIPseODMf7xmV5u6gjzkVvaS9eKZVkMxnKkFX3ZJmvOjaX43rFR2qu8vHNP85Imy+xrz8SwdFRNA1572WrLsicADAuGkiuX3hb2OollC/RFc+xtryj9vaAbXBxPMZJUMEyrtCo8nlLoncxwZjTF5RZ5nzg9zuGBGHdtqOW29TVEMipYtlKmYVpMpBWqfC4UzSDkcRY/c+4+LJnX+O6RYUzL4u27mhYtMz4QzfLDE6MUNMNWyOqq4ZHdjVeVOucSl9Z3HeyLcaAvxqb6wKLSkafz3IVJTgwn2d1SuWqK36ff3aq18NTYNwIOSSSWUYjl7HZJF3QCThG3LFATcJZJxU9nPKUQcMtL9mu83rkuKsNyqsGTZyYxTYtEXiee1YjldGJZlXhOJ1fQ2dlcgSAKfO/YCKNJBacsksjrdE9m+N9PXmQkoZAp6JwaWXgh7eWYSCm81B0tzWZNJ5Ip0BfJkVcNjg8lluXz5iOeVfnNrx7h9755DK9TQhQE0gWNsVQBw7TQDLvTvzCRYSiR5/RIkn98tpsvvdRPe5WPh3c20hr2EXDLSIJAJFOgeyLDgd4YX3ihjyfPTCz6mE4MJcmrBv3RHM9fjFDQTI4MxDk2GOf7x4btz5jMMJFSOFKURr4WpPI6pmnRHPbwxw9vxSmJ/OpXDvN7jx7nI184yM/Pjl92e8O0eKknitdpG9rt76gi4HbgdkhzDuhzqs7pkRQFzeRoMZ0Q7Gvlu0eGSeQ0fn5ugosTGVJ5nQO9MdKF8o48kinQG8miGRbtVT4+uL+NkYRCWtGZSBXoj2V5/mKEoXie0aTCQDRHfzRXuq6fOT/JVw8M8IUXemkLe/G5ZHTD4msHBvmPl/vnTBEYmEyU/f7ooUEO9cc5M5Lin57pXmhzl5ivfQCODiZQNIMzoylyavkDfiieYzRp36en57hPHz8xyt/85Bz/9+mLaEb59wi4HGQUnWhGxedaM7G7VuRUnYJmcmokVVLiA3tm/+mzk0yklZJk+RTHhhP24MWCwXj5QPz0aKp0bfdFyl/7ja8d48kz4/z1j8/SF720WmBYtumyUxYRlimJ69FDQxzsi/HY0WFGk/kl7WOq/kA3TPrjK1NrlMprGMWRorKCqVIjyTy9kRyqbvLixShgB6vfODjE114dRDUsDAvOjWd4uSfKV14Z4PmLURTN4NhQEkWZPcA1TIuvvNLP4f4EXzkwgN8l88H9bXzw5jb8LpkfHB/hawcG+cbBQTpr/Ny9qZZ9HeF51QIvTmSIZVUSOa0UfC2G0ZSCo1g3GsuqDCfyjCSUK294GR47tjRvpSMDCQqaybHB5Ky+caEc7rf3cXjas2s18dUXh678pjcQpmUxnChM+x0sBFrCPjbUBUr2D9N5qdu+1770Uj859Y0ZRF4XQY+iGYiChWZYiFyK/jXD4sJ4mtZKD5ppMhzP43fbktW7WyrxOEWiWRXFMKj0OnDKYkntBWy/l0cPDfHDE6OL6igsy+Jbh4d5uSfK944Oz3o97HPSWOFGEoVZrt/LTbqgM5TIc3YszdcPDPJidwRF1VF1A0kU8LmkknfRZFohmlaL6SYWXzkwwM/OTlDtd/KLN7UiigJhn5OaoBPVMKkJuJhIL74T39wQRBIFGkJubmwPIwgQcDv42dnJYvBp0FjhIeR1sLXx2rVPQTcYTuTpj2T53tFhXuqOMhjLkSvoRDMFXumJzbutZVm8eDFCXjXwu2Tu2ljLW+dQiXr+QoSvHRhgMJbD45BYV+tHEGBrcfXKsiz+f0+c58xYmuNDCfZ1VNFc6cXlENnRXMHRgQSff7abzz19kR+dGKXC45h17ayv8+OU7bSV5kove9srCfucuGURBDvImLquJ4r1FF6XTNDjYG9bZcnJOq3oKPrsjrChslwQwiGJFDQDRTfnrBm6Eoqqk1a0OV/b2hhEFAQ6a3x4Z6TmNVZ4CPucOGWRjXNICz9zbpKheJ5X+2IksuX7T+RVwj4nrWEvyfwbszN/LfA4JFtEptaPa1oKkm7aioET6QKjMwZ+92ysRRYFRFFgZ0v5qu6GugAuhy3321pVvgI/kVRQDYucZpadb7dDJuSWMS2L2uDyrKZMrZBkC0bJ0HqxOCQ77dXrlNhYtzJpJaslS1kWBTIFjcm0Unpe66bFiaEE8fy0cymLpT5LEOz3iIDTOffQ5PxYiiMDMc6PpcmrBt86PMS3irVAU2IF0YytFLirpYJbuqrnXXnpqPbhd8l4ndIs36aFsKe1ElG0RW1aKr1U+500hK5ODKgxvLTttzYFEQTYVB9YslT61kZ7H9cq8+Jq6axdk6yejigIeB3lfZWiG5wcTvJid5TTIym+e2SIx44Ol4STpsZzedUgPcfEwhuB62J9y067EnE7RWQBsqqJLELBsAeVT5yeoLPWh1O2g5z/8cg2Wqt85FSD7skMFR4nH9zfhiQKZek0hwfjDBYVbtbV+K+oBjadqX5FmqODcUgi772xFctaXOHuUnDJIp6iy/VoMs9EukA0p+GQRDqqvPyn+9fzt09e4HxxVqsh5Cal6Oxtq+Qrrw4iAE+fm+BDN7eXjv3DN3ewrbGCgVhuwZ4a09lYH2BD3SXn9bs31nJhIsPjx0cJehzcu6mWHS0V17x9plJUzoymmEgX2FAXoNrvpD+aRRaFsofvTH54Yoxnzk+QUw1u7armXTfMTnmJZ1Ve7bMDpxe7I7z3xlbetrOx7HslcxpD8TwuWaQu4OIXbmzhPXvt+ppETuOLL/ZxbixFXzRHyOPAxJp17TRXevnVu9aVfq/xu9neFMKyLLY3h6gPummr8jGRVmip9OAQBW5fX80NbZUIgsBoMs+B3hjNld451eaiOYWQ71L+f9jrpMLnRNMNGhZZtzQYz/FfHzuJopn86l3rZvlnbKwLMp5S2N5YMevcT3kVzXddbGsKklI0qv0uAp7yrqs17OWGtkqSeY39HasjNeP1iM8l85v3rp91fnIFA82wsCyL3IwZxvYaPztbKsgqKrfPuB6aKjz8yp3r5jzfunlpIio17V7VDRPDsvA4pHk9qxZLY6WHrlofQbcTSVzaoDHgdrC+xkNT2I8srUz9gbJKUjtL8vOKTmPFpYG8LAo4RaE0eRn0OLh1XRWWZdFY6SaSKlAXnLvPyasGkYyKYcJgLMsLFyf5VnFVMeiSuXdzHccGE2yoCywoPTHsc/LLd3Qu+TmU10zu3VSHYRi0Vfu4Y0PtrLTbxRLPLG0gesu6am5eQir6dO7bUse9m2uv+ZhlqWSVNSGDmdzYHubpC9HS74YJpmVL5//09Bi1ATc+l8ypkSR728PcVqx1rQ24qQteP2q9y8l1EfTkVIOkYisD+Ry2Oo4kCGiGgQWsq/VhWHYRYEvYS3ckS38sx/amIE5ZpKnCM6tuAaC5wsOxwQROWSxTuBpPKRwZiNNZ45/TzE4QBN5zQwt90exl63Vei84j6HbQVeu31a4smMyo+JwSkijidsq0VvmJZFW0Ys5DbyTDeKrA+hofjSE3r/bF8DllfnpqjLs31ZZmifa0VbJnDp+VhTL9u0+ZAgo77NSUqVn8a90+PqeM22EX+UUyBeJZFY9TIuhxIAsiAbdMTtVn5bZalsXFiQz1QTdJReOR3U20Vfl4qTvCcxci3Nge5u5NtfjdMmGfk1hWpaXy0szu9O/lc8vc2lVFXyTHfVtqy173u2UqvQ7cTgnLtMgodspbY8jDRLrAvo5wSWp6+j57JjOIooAsiWQLBi1VPpI5ja8fsB3Q97RVsneaAW1DyHPZIt76ULmfR8Ark8ypmCZILG7q+ORwsrSsfrA/Pivo+asfn+X8eBqPc4jPfWDPnHnF810X772xlZ0tlTSG3LMGF4IgrElcv0bMdX58Lgceh4RlQYW3/JxGMwVU3UQUpdKs/pX2B7aCn1oUpKkPXVrRsYpeZPbPS/0W5bRWejgsS1R4HUsWW9ANE0myrRUKhoGb1z7NstK3Ooq9gx4HWxpDpPIaW4rKk7Io0BDy0O3JIIggWFDldVDld/HIrib+788uIggCed1gLu05j1PCKYvoqoHHJReDbPs1zbRwyiJuh4jLsbigdanPodqAi+GEnZKbVAxGEgU+sL+1ZLi6FBoXKbQzneV4nq7WgAe4amW81yPnJspFQkzLTt/SDYvWKh8SxfuuWB87da+9kbkugh7DtGy5U+zlb8MCUwCvU8QpSzSEPGxpDNE9maY17OWLL/QiCgKfuqOTj97STtDj4NxYmp+fm6C50subt9UjigLr6wJ8IuTGIYllg6ifnh4nki5wbiwzp88A2P4Ciy18vBZkCjqT6QI51WBvWwU+p0TALbO/I8zb9zTx3IVJHKKIQxKQRYGcaqIaKl8/OMiGugAeh0SmoPGNg4NU+Z3c0Lb4lZ2Fsn4eN/Rrhccp8cjORlTd4Px4huFEHkdeoKnCS2eNl9OjKb7wQi+fuXt92XaCIFAfcvHE6XFuaKuks9pHNFPgH57ppncyy9PnJnDIAjd3VvP+fa1EMwV+fm6SM8/38pbtDdRPS3FwSCK/dHsn2YI+yyvHIYl8YH8bjyiN/MvzvRwbSpIrGHzz4BA1AbsYd64OandrJeMphTs31HJ7V5iXeuOcHEqQVnQ8Tplc4cqzhVPDAkkAc0Zq53cODaPo9mji8RNj/PaDmxfS3ADsbQ3zhRd6yRYM9s2xSpgpHpuqm6i6yWLM0acMd9dYfbhlAUm0V2dmqiwF3DKqbpDXzEXVW03VpgjAWFJlY1HBUJZEGio8GLE8rcs0ELo4kSGStoOzjKLj8i8+YBEEgXRBx+dyLIsPzFJIrZLUTrdD4kP728irBiGvg7xm8A8/7+a5C5O215llr8Tnp9UYVvgcZFW9KCk9t+j29qYQ3ZEsN7RWcNv6agaKmRq3r6/mGweHSOU1zo9n+MzdXXOu9jx3YZJTIyn2tFYuKYthOufGUhwdSJAu6DhEAb9LvuqVNt1cHSt1qxFtlaxiriamTOensLClq52yyEPb62kvmpe/UUUL5uK6qOmp8DrwuexZ+5qAC1EQ0HWTgm6R1wwOD8T5l+d7eOFilOODSRTNJFvQOTaUpD+WQwCODMTJqQbnx9Mki6kSiqrz5ZcH+MdnLpb+BpR0/H0uacn5sa8VhmmRzGtEMgUePzGKINiRfTyn8Tc/PsfXXh1AN028DntVoiHkRhIEO/9cFJAlAVEUCbjk0vc+M5ri8EC8JPu9HIwk8rzcEyU1T63HteKuTbV89JYOtjUFbD17oCrgZDytEs+qDMTmLlpWdZOtjSH7WlINPE6JZF4jmVfpmcjw1z88wzcPDuKQRJJ5ndGkQiqvcWokOWtfDkksBTynRpIcmda2Y0mF8+MZ7t5US3OlB9MCj1NkJJEvGZPOpD7kJuB2MBDL8rmf9zAUz6Gbtj/TnrbKWSse8azKyz3RsvqsqTNrWlDQyz8nkbs0G5/MzxbqSOY1Xu6JMpacXe8VzRW4ubOa+zbXzZl69LFb22kNe3n3nqZZQSDY/kRffuWNW2S52jEsi5d7orOK/QcTeWJZDcOEkzNEKPKaiVScWFIKC09Rmd731k1LkbIAr0NCFFg20YqJdAELOyifKbCxUKxiyh1YK+cpYq6e++Z//fQsn/r3g5wbTZJTDXKqzlhKQRBELOz2kkUBRTN4tS/GtqYQzZVebl1XjThHiqFl2UL7Ya8T3YRYpsCh/jiH+uPEMoXS8yvolpkru800LQ71x8mrBo8eGuJLL/WRzC3d8PLMaJq8ZmAYFkPxPFlVJ3CVssqxqzie1zuRzFrbTCevGlhzjNGyBYPBeJ6LY2m8zksqbefG0hzqj6MvUeji9cJ1Ef45JJHOah/Zgk6lz0kir+N2iEUpSltxLKcayJJIbdDFrpYK0nnbJ+DpsxNYlsXmhiDjqQKNFW6Cxc7xSy/38x+v9GNZFum8zh+/dSsAD22rZzCepzbgKpstUnWzVJC7WqjyOwl6HIynFGRRYDKtUhN0MZbM0z2ZI+xz2AVrgkBeNWmr8nLruhqiWQWvU+a37luPxyGzoT5AQ8hDbyTLj0+OYZj2TPz+K/jJLISCbvDtw0NohsVANMcv3NiyDN98YTgkkVu6qslpBpGsRjxTYHOdn75YnngO2uaRL9/cEOT5ixHaqrz4nLZnzvoaP72TWQRBIFUw6Ivaxp3NlR5CHns2c650yCkujKf56SlbMc60LDbWB/nOkWEM08LjlHA7JFRNYyCawymLjKcUJlIKtXPk3g4nc5wZTaObJgXdZEN9gNvXV9MS9s167/eOjRDLqhwdTPCpOzoRBIGpLAZJBI+zPPiYHowE5whMfnhilLGkwqH+OJ+8o7NscNpc6S21xVypny9ciBDPqRzoi/PQjsayVdSDfTH+5fleANJ5nU/ftW7etlxjZUjmNF7qjs46936nbRhtmdas9CJZFDBNE82wcMgL7zvdskCmYPusyNPm53TDKqkAvnQZMZLF8PbdTeiGRUe1d8nF6IIg2M+k4mB+JfC5V86bZzo/PTnKF1+0n62//O+H8DhEZEnktvU1jCVyDMWzIIBTEnjm/CSnR+xVE59LJqXYqpszn7OiIFDld5ItCvX86Q/OcGokiSQKfPvIMJ+5ez1D8RwNIc+caVpiURzmhYsRjg8l6I9mGY7n+c8PLXwlezpv3lbPi90RxlMFbmyvpNLrJJFTr8r81Lvm0zMvXtfqSN1cLWimiccpwzSRF4GiFYVu8E/P9bKtuYLOGj+DsRw/PDEK2HV/t76Bfeyuizssq+q4HBIpRaejxpZXrvA4irUNtjt3NKtiWbYCidcpo5tmycNBFAR2tlSwvSlU1pFKRVd6i3ITU1kS6aguHzyeH0/zoxNjBNwyv3hT65ymkCuBQxK5fX0VY8WZ1xvaQii6RTyrUul10FZlD0LHU7aKzs1dVXxwXztfeaXfzp/WTLpqvaXvLwqX6n4ckrgsQY/AlICExUqlDN/UEUbEHliPJhVEQeCO9dVlLt5g5+X3RLKsq/GX1HkABCzaqnxsqg8Qzao0V3p4zw128OZzyXz8to4rFsROf00sXntTf5FEuz7r4ngGQYCUotNVK8y7v3s31nF2NG2LDngcHOiNcmQgzh89tHlWGuHUpT19DDEl9SsXi4qnc8f6Wp67GMW0LG7pnN05Tu1HEGYnoPiv0BYXJjKkFXs2XdGMsqBn+j24yhdY37BMnaGZp7Y2ZKsKdk+keXhHucqhZVoIgoiAwWIWj0VRwiEZiIJAwShfwfC7HTgkkZB7eVKMTctOWXbIi61im45lG5SKYkm++rVGEgQkwU4NdKzg5JwkiliWPbkjIuB1yvzGPesRRYGnz47zan8CwzARRVvxDuz+cer452NbUwWVXicv98QwLYuCZhDyOmmrsoWMrqTC9uDWerpqfFwYz2Ba1py1vgulo8bPn79jG/GsSl8sT4XXVo+8Gqp8b8zi8oVQHVj5coLVhNshIQgCou2JjCzawla6aY8LBeHSM3X6uHcVl229Jiwp6BEE4f1Xeo9lWV9Zyr7nwuuQWV/r56b2MO3VPr7yygCpvI7f7aSxwsMt6yp59mIUiq7fmmHhdznorPHRVetnS1H6d+bM0UdubkeWRAq6wfv3Xd7Vu3vC7iSTeY3xlEJ79ewZ9ZXAsiCe02is8OB3ydyyroaRpMKB3ij7O8PUhTy8Y3cjT52ZwDAt3r+vFRAIeuwVoHhW4/vHRpBEgQ/ub6OtymcrfjhtSdjlUFhzyiLv2dvMUDw/pxTxtcYwLb5+YJBYtsDRwSSmZVHtd3Jje5gbWsvFGp46O8HpkRROWeRjt7aXloZlSeR9N7WyuTGIp1hbMtOL5krt1FXr5+EdDRR0sygPKvDuvc2MJhW2NAQZiOXwuaSSUtU79zRRE5h75nZrU4g/eetWhhJ5XrwYQTMsNMPg6GBiVtDztl1NXJzI0FHtKx3j1KBMMy0008A9rSv4xf1t6JZFIqfxqds7Z332W3Y0cm7Mrp+bb9AwX1s8sKWOn52boL3aNysVZHdrJb92TxcTKYW37nxjF1uuVkJeB3durKG50lO2wqdoBsPxPKYlcGasvLjWEuyUYcMlIy1CDvq379vAF1/qZXN9gI31l2R0PU6Jv3zXdp4+M8G7b2i++i+FvQoLtmx1IqfNe99dDkEQqAm48DqlFZOOdjpFvE5boGUla05v6gyzt72CkXieT93Zzv/6xqXnb7XfTV3ARU41WF8X4M4NNdQEXIR9DmJZjaZKz5zZFKIo8J4bmnn+QoQLExkyikZ1wE1XrZ+tDQt/rqyrDfCf7l9PXzTHQ9salvwdeyNZvntkBLCDqS1LtF+Y/k27al/75+NqZnrbbLqG9hbXIw5JpL3aRyxrZzo5JIEKn5Owz8GOpgoe2WULMIGtkvnIrkayBWPJ1+nrhaWu9Nw/7ecGYDvwM8AF3Af8FFi2oKfC6+DeLXWcG0sTSRdspTJgV2sFn7itA5cs0RdVMC2LCo8Dp0NEFgVq/C5euBhhNJknkdNJ5jXevK2exqKShSyLfOSW9gUdw67WCibShaJXytzFs6pu8r1jIyTzGg9uraO58tp7NVhYtFR6CftctFd5ubE9zM/OTtBY4WEokSenGlT6XHz01o6y7fZ1hnn67AQTKQWXQ8QwL8mdvmVHAz8+OUY0o/L3P7uIUxbZ1hS6qiXR2oCb2sDKzGIZpl375ZQlOqp9BN0yLodEW5Wv9HDNFHQeOzrMob441QEXggCabpWZqtcEXNy3eW6365Si8b2jI5wfS1Hld3HHhhp2zwioYLaYQ0PIQ0NRsWdDXYCuGj+JvEbI47ii7Gpt0E1t0E2l18GFiQwOSeTOORTMHJLA+fE0J4YSvGVHIzXF7wcgIiBL5d1ATtWRJVvdLlnQqZmxqul3ydywRGW/B7fVc1NnlW2GO8f3mylpvMbqQhQE9sxxXedVA8OyJzhm1sS0Vnop6LaMakdV+WTRRFrh8eOjeBwSj+xqKltBf9++Vh7YVk/ALc8KrvOqQdjvnGXuu1T2tofJFCZoCHmo9i8tWHBIIpNphc0NQQJXkeJ0NQgIhP1OxCxL/h7LQV41CHps+e+cWh4B+l0SHqdcVPqzfbl2tVSg6gYvdkeov8xzosrv4uGdjRiWxZGBOLIoEvQ4yOvln5HIqfzZ42dIKxq/ds/6Wd4zN3VUcdNVSttPr7m8mhrEqZUuUYCQe22JezrT26bau5beNh0BePuuRg72xRAFobjaI3JTezUfuaWdlhmrjkvxono9sqSgx7Ksj039LAjCz4A9lmWNF39fB/zdlfYhCIILGADOAMOWZX3gcu9//PgIZ0dTyKJIR42PsNeJSxL4wgu9tFZ58ThEWsIe9nVWEy7OcH3+2W6yBYNnz0cIuR2IosDxoWQp6JmPtKJxZtSeyZ5S4moIea4YIA0n8iXfnxNDydck6BEQqPDa6U35gk59yM2dG2vwuiR+cHSUtKRzsDfGXZtqOTWSRNNNLOD7x0bonsxS7XNy96ZaWsLeUrvsbq3k7FiasaTCSz1RtjeFeLUvxi3rrs4H4LXGsux0tvNjaYbiObY1hrhjQzU/PjlO2Oco84/onsgwkSpQG3AR8Di4d3MtoUV0shfGM4wlFc6NZ6jMFIhlVTbVB+dMg5xIKfRFc2xqCMzyzZkyiJ2iN5Illi2wrSmES5YYSeQZiufZ2hjE55KLQhY6v3LXOsJeJ33RHC6HVJZX3jOZLYkOnBlNUROoKaW3SYKFahh4psnr9kxmOTmcRDcs2qqS3L2ptuwYU4rG2dE0bVXeRWv9nxpJ8rmnu7l7Uw3v2du6qG3XWL3Uhzy8/6ZWXrgY4dN3lK8OHhlMoOoGsijwSm+MOzdeup5OjaRI5DQSaPRGsmWzkM9fmOQvfnSG27tq+MNpdReaYfL1AwOcn8gwGMvOObmwWDqqfXRUd1z5jZchV9AxTOiLZOetxbvWCKKAZZpkCjruRdRPLTcW9mSLZVmlSUqw+76TIylkESzLpGciw2NHh3nrjgZeuBhlOK6QLUxy2/rqOVd7nr84yc/OTPDuPc08squJA70xLCy2N4VI5jTOjadpr/ZypD9RUnZ74vTYNTHc3NIQ5NRIElU32X4V+59adTctiC9C6OONgDWtbSK5NfW2mTx2bBhVN9BMe+zgc0rsbKmYFfCscYnlqOlpByam/d6DvfJzJULADyzL+sSV3pgt6MWCbzu67Z3MEPA6bPWK4uxiW5WPt+xoIOxzouomTllkc0OQV3pi7G6pJJlXSSs6G+quHO3+8MQoIwmFV2WRX769E6e8sNmX+uKse1rR2bAIo9OrIa1ofPmVAfqjOQ4PJHi5N2oXt2ExllTIFgxUzSCaLfCjk2M4izOmL1yMkFN1Ql4n/8/DW2alQmyqDzCeUthUH8Ali2yqDy57wDOlYLYQI7mlkFI0/r9nu3nuQhSfS6InkqU3muH5C1EM06JnMsuHbm6jqzZAa9iLzyXhcoi8c3dzmez0Qmiv8hIo+vZMpBQ8Dpknzozztp3l9Q26YfLNQ0OousnFiUwx3dD+uygI/OTUKD87N8ntXdXc2lXNY0eHsSyIZTVuX19dEoQ4PZok4HIQyRTonswQzajFQM7LhYk0Hyima6q6QX8kQ28kS3Olh3VFcQGj+DBRDJBmPGcLusHh/jiaYbKvc7as6w+PjzKaVHi1T5wlZHAlPvKFV4lnVZ48M85tXTUl/4A1rm8KuoFpWextDzM2w4un2udiIlVAM0zcM0QOumr8nB5J4ZJFmmfIT3/ii69SMCxOj6S5f0sdNxS9pwqayRNnJtBNi7Fknv/+9h3L8h00wxaqWWo/pxkWY6k8PqeMa4UCjmRGYSBuT3AcHkxd4d3XDlGA0yMpUnmN9cVnrmFaPHp4iDMjCY4NJTEt6I0O8+TZcTJ5nWimQH8si2F55qw7UFWD3//mcXKqzrPnJ3nyd+7i5nWXVmu+d3yESLrAoX6Jt+9uJORxkFN1bp6jLnE5uDCRYSShkFcNPvdMNzuaQty9sXbRQkfT16hODya4qX3NXHmK6Y+mY/0x7thQO+9732gYpsWZkRRTIqkF3aI3kuVnZyfY31m1pBTdNwLLEfT8AHhMEITPAxrwUeDwArYLAumFfEAir+EqnlgLyGsGVt6WoJQlqZT3KQoC3z82wjPnJtjcGKSrNmAXc0kCH7mlHdNa2ABbmJZJupjnn8cp8dFbOzCKnkLdkxkqPLb52rVk6hAtC6JZFTmvoWgmimbglEW6I1mevxghllVRNAOXLKLotrFrQTMYTeZnBT27WyvZ0VyBJAoYpnXFdlN1k/5otiSnfCUm0gqPFt20331D87VJfbNgJGmnPSq6iQBE0gUSuQIF3eL0SIqnz07SVRtANy3u3VRLe5UPaQnFrVV+F5+8o5O0ovOFF3ptk7A5mmy6ctrU/4OxHI8dHUZA4JXeKAXd5DvpYXZM86SZuaueySyVXidD8Rzdk1mckkBW1Wms8Jau35PDSb59eMieQW8IsqkhSNMcQUaioOCfdv4HYzmSeQ3TsjvR2d+h/P/FkM5rpbq7kXh+VtAzkbYD9fYq73W1qrjGVB3X7BrAWK6AJIKFQHKGj0xL2Muv3LnOFsWYsZ1aNOqxgO6JVCnoSebV0ux4wShPa1oqr/bG+PeX++mq9fErd3UtyarAwrKLigFFM1n+tYUrM916YSXpmcigaAYOSaR74lIfIiAwllIvmcsCaUXnp6dHaaj0omhGyUh7JlNpyoZplQyQpzNdZKMu4OYfP7gH07TT2K8FU5frcCJPwC1z3LLrNtuqll7ve2wovkxH9/rjyFBipQ9hVTElsT8dzTCLfenKHNP1wHIEPb8FfBz4MOAGXmEB6W2AB7hfEIQXgX+2LOtfp78oCMIngU8C1DY0lRRpRKAm6MbnkmkPe9naFKSz2s94ukDI7eCxo8OcGU1xoC/GrV3V1AbcdE9kMMyFK7U8tKOBc2MpWiq9pYffqZEkowmFve2Vc/qLTEcSBZ67MMnBvjiyKPDhm9sXlSq1GAJuB3/45s185ZU+RpMKXqdE90QWpyTiku08z6BbJppRiRVVxxoq3Ki6yWhKodbvYjSVZ2N9sCywyasGL/dECXrkBRmWPn5ihL5IjoBb5mO3dlwxSBqI5igUpyj6o7lrEvQEPDK3dlVjGBbpgo7XIdATySKLIqpgMJFWsLCIZVW+emAAw7TY31lVNnu4GATBDhDrg24csjhn/Y8kCrz7hmYGY7mSvHX3ZAbNsDAtk6DbwWSmQEPITXOFh3fubiaSLbCtMYRTFnn3DS0MJ3IUdJNXemJ01fip8rnIazrbmkJ01fpLdUPnx9O4ZAnNsIO+pmm1aNPPTm2w/CGtmyYpRcOyIFeYPYiyhQxStIS9ix4c7u8Mc6A3TrXfxZYZKSETaYU/fPQ4Oc3gk7d3cs889VNrrD5cskRdwMmLPTHumlFXZpn29WeYFqo+e7A638z4pnofZ8ayuGWBB7bWl/5eH3RR6XEQy2l0LFMaxz8+083ZsRRHBxM8uKWBjYsojJ9CFOxVIqcsXpUq2NXQfBUD7uVkY0OQ9iof8ZzKHeurOc2lvs8lw8G+S4N7lyyyf101L1ycJJHTkKU8ljV74OZxy7xnbwsH+2I8uK0e3TB5pdeWLN/XEeatOxu5OJGeVqspMIfdz7KxrsZPQ4WbjKIxni4gZwtXLR7x0LblEeZ4PfLwrsYrv+kNhKIZuCWR6eth66q9PLCljmODCfa2h69KPv31ylUHPZZlmcA/F/8tZrsTwFZBEHzAM4IgfNeyrNi01z8PfB5g647dVnudj+5IFqcooGomdSGJzpoAv3HvBhTN5O+eOl+cxVcRBYGAy0FDyI3XKbOpPrioh5BdqH1poJ/IqSV/lXRB4x27r9wxZRQ7AtdNC0U3CHFtLj5BsGeXtjRWoGhxKn0OKn0uhmI5JjMF6gIudNOWsJZECHsd7O+o4g/etJlnL0xyYSzD8cEU1T43O4srC9FMgYN9MU6P2gtx1X7XZWevopkC8aJWfE41FrQytKE+wPnxDBbWZb1trgZREHhgSx0vdUfJpDQO9quIokBtwIXHIVHhc5JRdDKKTlrR8DikWTMni+WpsxOMJBUEAVTDLJNknmKmqMPWxhADsRxuh8Qn7+jkhYsR+iJZnj43yX2ba2mtsgd2U+26q6USSRTYXKwZkkWBRF4j7HWWDSD3tFYSz2m8c08Td26oKVtxLEllA/mCjnOag3w0rSEClmAruM1k5v2xGL7w0Rs5OJBgU31gVr3Ts+cnOVdU0fre8ZG1oOc6IpFT+eahYTTD5Isv9rF7mtCFbgGmhWlai1I1+9it63j2/AT1IQ8O+dKjKq8auB0CYa8DQVielR6fS0ISBVyygHOJqWlC6d+V+79rRXaVrPRUeJ187gN7iGRV1tX4+cfi32sCLvKqWUrpCjgFfumOdXxwfxtPnR7HtCxUzSjWcsxuw99/cGOprzs+nORAMejxOiV2t1YuuV9aCmdGU/RHcgwnbLsIn0MimdNm1WkuhrFU5spveoMyHp3bSPyNimlahDwOEtNWz3O6xYsXI1T6XGRVY1Z6/RpXEfQIgvAEzG9pYFnWA1fYXrYsSwdyQJ7y9M0ysqpOTrdwyDJuh4hqWOQLBr3RDM+en8TrlDgxlGQ0qdASdlMfdNNa5eXTd3aV1eMomsGpkSS1AfeiCr1csl3rUdBM/As0yLp9Qw1uh0SV37noYu/F0hvJEs+prK/zk1d1wj4n8ayKZloYloVp2Y1b6XWSLhiMJBX8LpntjSH6I3axp99tXwovdkd4pSdGIqfid8k4Zakk2zwXB3pjvHAxAsDGugCbGgILqoEKuh2lepbFEs0U6I1k6ar1X3HVbSSRZyJVIK8aCIJA0OVgb3slhmEhiSLNYS+HB+Kk8jqmBbd2XV0+daDYji5ZKtVPzaQ/miWSUdnWFMQlS9QEXHz45vbS6wMxO2VtMq3idog0V3rpqPbx2NFh+qM52qq8vHNPc9msYvUcKZTt1T4+cdvcxdnWtP9nmkmuq/PhctiS5e01yztzrOgWUtEoNzQj025Lox3EqbrJ9saVSA5aY6lIosBEWiGt6Phds9UA87qJaVqLcpx3ygIep1xcObk0AHZKEn63EwSdimXyNfn0HZ389U/Ps6ulgo4lqhwZpoVmWORUE01fmYJ0r+vSvbzSOmAhr5NQsX82LYuDfTGaK71sqAvglAVM02JjfYjfvG8DYPskDcRz+N2OeVOM4zmN/mgWR51Ydp0tJKV6uVE02yJgLJnHIYtkCsZV+zO1Vq0pbM1HW81acf50fC6ZwgzVQEU1uDCRZVeLg8Ca0e2cXE2r/I+r/OybBUH46+LP/2FZVmK+N1oW3LG+hkROY09riOcuRBhO5JlIFzjUH0c3LbY0Bm3Tw4JJbyRLIq/xxOkx3jLNKO9nZyc4N5ZGFAQ+ems7ggBPn53A65S5e2PNvKtBHqfE+29qJZJRZ5mWzoffJc9SvboWZFWdg30xHEWJ4WeKQWCl18mtjUF00yJb0OmP5miu9NAa9qHqZrHANMB79toGV1O1HqMJuwi2wuvgjg21tIa9ly2IG0lcmn25ob3ymgd4AN8+PEymoHNyODlLins62YLONw4OYWFR43dR6XPw/n3tvOuGZju9L5mnJezl/3u2h/qQG1kULhvgLYT7NtexrsZPjd/FaFLhYF+M9XUBdhVX0WJZle8cmRInULl/y+zVjLGUwkAsh2lZ+FwSh/rjfGBfG6NFBbap/8EWQFjIKuZ87xMEUDWT6R6PjSEv926uQdVNdrUsXhnLKgbac812//DEKEPxPE5Z5Jdut+Xmpwh5nNzUHiavGmxqWF4vgdFknhcuRmmu9CyL4e4a5RimZa80CnbfMZ2sqiMJtrKYvogaHN20cDts80pzWgzhdEr8p/s38KOTY3zk5sv7qy2UL708wEA0x2S6wHv2NtNUsYQBVtEMUBQENHNllKY0XUAWBXTTmjWZ8VrzwsUII4k8d26oIZnXeO5CBIck8KZtdTSH3CQVnXs21zCZLvDchUnOjNrCC2NJBV03kOXylWDTtPjW4SHyqsGZsTQf2t/GL9xoG0TPVas4HwvtM6+EQxLZVO/DIVq4nbYR+NWyXBLsr0dy2pqy3XSyBZ28cul6CbolWqu87GgOcfuGavZfpST765Ulj/Asy3oGQBCEvwW+alnWK4vc/jng5oW8N+B2IAoCTknkO0dHODuaxuMUqfK5eOL0OB++pZW8auCUBQbjuVJNwqmRZFnQM5PD/XGODyWZTBeo9jtL0qenR5KkFZ190wZHFV7nFVcVVoKCZvLdo8MUNIO2Ki/pvO1HFPY5ubGjgq+8PERPJAsC5DSD3kiOxgoPT50dZ3dLJdFsAa9TLj00bu2qxroYoTHkLnmxTKRthZqpFLdEzq4Pagt7aan0kMiprKv1vyYBD9gFw/b/l0fRTBI5Fc0wUTSLrGrwo5OjPLKriXhOxeeSOTKQoD+aI6vqfOqOTg4PxJEE2N5UMavWoD+axeuU5wwCJ1IKimbSWuWlq6iQ9u0jQyRyGufGU4yn8ty6bm4VoYFojsG4Lb3rdcpU+11sqPOjzOjk79lUy4nhJNubQliWxWNHR+iNZNnfWYXLISLAnPK9J4aSPHV2nLqgm/fc0Fz2wDeL2v7TqQu6ODuWJqvofPquriu0cjlpReMvf3SGRE7nD960qZSat1BqLlPbpRsmfdEctUHXolNInjsfKUnKb6wLrKhx4+sRp2TXy6Xy2qyVnj2tlQQ9Tgq6yY0dC08/Gohl+ebBQTbWBfjM3ZeuQ80weezoCLGsyqOHh9mzDClNI4k8Bd1ANUziWXVJQY9blnBIInVBF2Hfyikn6UVVzJn9x2vJcDzH//zxWSJZlYHYJSEDtViLOJ4qoFkWRwcS1Fd4efb8JLpprwKHfc5567x6JtIc6IuXTEUnUvYE0JWCHsO06ItmOTpgS1nvaq3g7o21jCTyWAvYfi6cssCBvgSqbvLA1gpuaAvTcpU2FdLsbOg1ilhritVlCMKUj5F9v6cUg0haweeUkAWBvGbgc8nkVYPhRJ7mSs+c6fZvNJZj/esk8P8KgtAJfBs7ADq5DPstkVN1vv7qAAXdRBRAM+3OM56zo9z//r3T1Fd4mEwXcMiirfCmGrxwIcK/+nu5d3MtbVU+7tlUS23ARV3QjUsW6Ytk+dGJUURRwCEJ7G6t5MhAnL/68VksC953Yyvv2LO6neFV3bBnxgyLZF5DlkQKusGRgTjPXYiUCkKDbgceh4imm/RMZjg+lKDKZ6t/uZ0S+zurWF8b4MM3t5W5nE+kFD73dDeRbIGN9QG2NgY5NZJCN+xZWEUzkUSBrVeRjjSeUjgxlGR93cKUb961p5meSJauK6ShOCSBoXieRFExTAAO9cX4ra8dwkLEKQtU+1wMJ/IYpsX3jo1wejSNAHzkljYe3HrJrftQf4xnz0cQBYH372stBT551eDbh4f48akxWsNe3rO3pbSq0xDyEM2ovHAhyoGeGF95ZZDfe3ADtQEXlT4nd2yoZiCa409/cIpUXqM+5Oah7Q2k8xp5zeT2riqawz7CPie6aTIcz3NzZxUtYS951aA3kmUypfBnj59G1Q0qvE5+5a6ushXGM6MpvnNkiKyq0z2RYXdLBZsagmXpbclcFq+norTN3z15nuODSSzgD751nO/86q1l7TqRVjg+mKSzxjfL8Oxbh4b41qFhTMtCNw3+6UM3lr2+r7OK6IlRdrVUlK3ygD3weOvOBtKKPqfvxXeODPPshUlqA25+/00bZ21/ORoq3Awn8gQ9Dnxry/7LzrnxNPFi/dep4XKp5LSioxkmejGgWCj/+6luAI4Np3ji1Ahv223P6luWxWjSXun3uZbnIf6mbXV8/pkcrVUeumqXVmOo6AaKZjCeUsgqOm7Ha3+dZfOXAozlqXZaGqdHkxwfTmJZ8M1DQ4Q8Dm7pquJrrwzwrcNDZIsB2QsXI+gWnB1NkVN16oJutjQGEOdQINA0lW8cGgZs4QmnLPL8xQiSKPCpO9dxz7R+z7IsDvXHyaoG+zrC/PzcJKdHkhwaSLCrJcTZ0TQtlV4+9/OLYMEv39HB5obFPcOODSYxi3WWQbeTezbZq/YXJzL0RbLsaq2YM+34cjx1cpw3r4kZzMmTZ8d4z77lWdl9PeB3yWRmpNH2xRT+8dlufnp6nPu31vHrd6/nm4cGiWZURAG2NYXY3VpZ5gX4RmM5hAz+GfhnQRACwFuAPxUEoQv4GvBvlmUNX+1nxHMqoWInOZUdMf1U53SLoXgOsNWz9GJu9YnhFDm1n0ROJZIpkMhrfPrOdbSEvTx9doL+aJasqmNZFk+dneDT/34QC3t5vcrnJJJRZh7KqiOnGsjFRlF0C6dpG1VpxqVpEcuyZ+ALuoEo2LNeXqfEhWwBzYBkXuf7R0doDns5PpTkTdvqecv2BmRJZDSp8OyFSXKqTs+kPZObUXRqAi5e7okykrDb6t5NtZe9kWJZlcdPjOKSRN66s7GsiP2HJ0ZJ5DTOjqX4lbu6rlgEXOV3LUgGXBIFNMPEnCaPWtBNDg8kUDTbk6O9xodTkqgNuohnNXonMxR0kxcvRNjSEOTF7ignh1NkCzqqbpBSdKp8Dj6wvw1BEHipJ8J3jw5xfizDxbEUL3VH8btk/svDmyhoBggWhmkxkswTyah89okLmJaFxylx14ZaUorGZLrARFphIl2gyufk5HAKr0smoxrsaqnghydG+f6xERorPJwbT/Ord63D45TY0hDk78+ME8uq5FR7kHV8KMG3Dw8xGMtx16ZaNN1kIqXwal+c+pCbF3uis1LHzo/laaiqKP1+ZiRVur8GorMlq39yapxIusDp0RSfvnNdWQ3Xkb4YSrEjPjE02yfkL354hgO9MaoDTh77zG1lM0+qbnJ8yF5lbazwzFo5PNAbYyJVYDJdIJnTqA0ufMB7+/oaNtUHCXrkBfturbFwppXczCrOfPTQAJMZO9j52oEBfv9Nm0qv/fT0GP/1sVO4ZZEvfOxG2qvnnsj47qG+UtBjmhanRlJohkVaWZ50oJe7Y+imxXBCYTSZp2Oe47gcBd1ENSzUvE4kU6DqWsjwX4GzkdVR7B3LXpKlVnUTVTc53BfjqbMTJZEfgKxmcaAnhon9LEspWXoms/zp27bNquuJZC9dWSa2JH8yrxL2uZhMlz+rX7gQ4de+dgRdN/nIrW20V/kRBIG6oAu3Q2JnS4inz45zpD+OBbzYHVx00ON1SqUJs0N9Mb4bdHPf5loePz6KaVmMp5WSX9pC+fHRMf7mfYva5A3Dk2ciK30Iq4qCblKYY2ZDUU3ymk4qp6ObZmnS6cSw7Y0VyRR4741vXGPwZXn6C4JQDbwLeC+wHngc27PnKUEQPnW1+5cXoITjlEV8Tgm/W0YQLs1yJfMaA/EcFydsA8fHj48CdhFYLKtRW1Q3E4BjQ0mSOY3WsJfOaj/vu2nhF0a2oHOoP15abn+tMGdc9PPpsxuWfZOYlt02umlRG3AjCkXFIUEgklExi6adI8XaHsuy8Ltk3A4Jl2zXvNy5sYa2Kh9BjwNBsI0uk4o9yzsYy3FkII46Ywbi5HCS0USeg/0xXu2Llr02NfPuccpzetssFa9TwjtDIUwWRQRBsMUdLEjn7QBua2OIh3c04JREAi6ZI0NJ/v7pbp46M86h/hg5VWcyo1IbdDOZUUnmNbonM4wlFQzDwiGLKLpFWtGIZgt86YV+eiJZCpqJQxbwOW3j0oJukMhpjCUVTo0kCXkc+F12SlvI48Cw7ILeoFsmW9D59qFBnjk/gaqbnB1LcWwgztPnxknkVO7ZXMvutkqaKjzUBuwZ0ryqc3wowWSmwJOnxjkyEOfsWBq/2z6Hhjk75SVbyJX9XlZ2MUen6i/Ornsc0uwAddqvc923J4YSqLrBaEJhKFYeUA3EsvRHc8SyKseHkrO23dteSU3AxZaG4JIk4GsCrkWtDq2xcCq8zlLgMzO97aXukijnLDXALzzfS0bRiGQK/NuLffPu/4aOS7P4o8lsyctluYKeWE4lkbfv67lktRfLs+cnrvyma4CwSmpCbmyvIux14pZFbmoPk1UN0oqt7DmzB9JMs6zfNyxIZgvMpDZo7w/ALQusr/FR7XdRH3Tz1u31pBSNQ/0xIpkCPz41Rq6goxomT5+d5L7NdWxrCvHLt3fyS7d1cmo4xcH+GIZl4XZI+KbVco6nFA71x8mpl2/LwXgOt0NEEmGyKK4zllLwOO1jnHkfLARhid1Tetp3f72y1nOXM9+5dkiABa1hDw5J5K07GtnWFGJbMXvijZ7pcNXfXhCEF4BW4FvAX1mW9fK01z4HdAP/dLWfczmcAjSEvGiGSVbV0XQ7iPG7JTpr/NQH3WQLBhlFK9Xp3NheiaobPHfBzaH+ONGMQtjnorHSw7oaP+/Y3VSmCGOaFgf7YwzF8+zvrKJxRg7w4ydGGS4WaP/y7Z2v2WzyTIfzy4kGyaKAYZoEPQ5Ey65xWV/nR9EMPLLEpoYgLWEfIY9MbdBeSWmv9nHf5lpiOZUHttTTWuWludJu60imQDyr0lHtp6PaRyyr8q3DQ7ZJakblvmlF+p01Pr5yYICMonOgN86+zqrSAPRtOxsZiOWoD7qX1ZCyoJvsbankZ2fHS67FhmXLPAbdMiBQH3LTGvbSVGEXuO9urWQ4nqPS7yLkdnBsMI5mWESzKg/vaCCW1WiscJPK63zv6Ahgce/mOvpjOWRR4OUe+0F676ZaksVAuC7gpsrnYn9nFVsb/fzeoydxSIKdmiEI6KZtKPaW7fXc1lXNS71xxpJ5eiezPBXJomgGlV4n8ZzKcDzPZ5+4wDt2N/PRW9r5nQc2crAvBlgcH0oxGMsScMtkVYPGSjeGCZVeE69LRhIEdsyRhritrbzWyOe+9HiZS773oe0N9EayNFV4ZgU9H7m1g59fiKAZFh+ao8i8vcrLSz0xqv0uWivLUxnrQx78LolsQWfdHKpx79zTzJ7WSmoD7rXgZZWh6VYpWC7MCBree2Mzn33STlWbKQRz3+Y6Tg4ncYgCb97RwHysn7byMt1Xarl62WRORVFt9S1riaUw0++Ed+xcGbn1hvrVof4VcDvY0hgkmVfZ1VrJM7KIKAr4nBI51SibS3FJor2yXTQcFYFwcHaNjYBATcBJLKvSVOmlKujGGEwwllJ48twkqbzOZLqAxxnn4e31PH5iBE23eNuuRip9Tu7ZVIskCowm82QKOq1hH7pp0VntL6UEK5rBo4eGUHWTvkiWd90wf6rZumofumHLsFd6HYQ8DpoqvLzvplbGk8qSTEpvbFl8bRHA946NMJEq4HbE+dQdnfPWRF3P7KhfW6GfTtjnJDfH3wsG9EZyfP65HjY1BtnXUUVrlZdbu6qZSCm0L1CMay5M07rur63lCPm+Cnyu6NcDgCAIGyzLOm9ZVlYQhLdd7QdYxXqM+XKUK31OKr0ORpJ5UjkNE5BF2NIQZF9nFZIosL+9koSi014srBYEgdvW17C9KcT/ePw0lmVx54YaPnPPeizrkpGpqpt8/dUBnjo7QSRdYFtTiIJu8oszVoGsq5SqXCozJ+4Ni5KRqyRQTGez0wE0w8Ih2T5HhgUWBj6nbBcaux1saQzxwNY6ZFEoBR8VXuesNgFbueaTd3Ty4ZvbcUgCsiQSnTbzMFO6s7nSy50bahhJ5HHNCAjdDglFM/jXF3ppCLl5157mZVHXsYC2Ki9dtT7OjWWLM4wWk+kCTlnk3k213Le1nnhWY39nFV6XzGffu5O8Zi8FD8RyuBwilmnhccl8+Ob2UhsOxafSSAR2tVbyyTvXIQkCqm7wal+cQwNxGkNuwl4H4+kCXbV+fvXuLj7/bDcOScCyLHomMgiiwFBcwe+S+NHJMZ69EOF9N7Vy/+ZavvbqIGI0R33QhWHZhcmmZfudHB6Ik1I0fA6Jl3tjFHSDoUSeRFZjfZ2f//a2bcTzGt8+NIQsiVycyOAQRR47NsreGaouY3GN1ml/6qrx8twFezVurgLfw/0JXu6J0lnj4207G8sC1T1tYV75g3vJ6DrV/tnpPUOJPA5JJK8ZjKXzZRKtiqZzsD9OWtHZ2x6eVS/kkMSS8eoaq4ts4VKtjqqX3/v3ba7n88/2ohkWj+wuF5apC7mo8DhwOSTCnvnTY3PGpY5OM+yVAdOy6/aWg0jWfm6omslkRmXTFbeYjc8l4xChNuimwr8y8rrJOXy1VgYLQbz0s88l8+k71/HTU6NEMmrZs7wu5GZzXZAjQ3GyBYOOGt+cgyvDtMhrJpZlZ1bYGQr2a6ZpP4MNw+A/Xh7iS6bFhtoAe9oqeWhbI0cHE/z83ASNFR7eubuJPW2VTKQUHt7RSKXXUZZKO/Usv5L89FAij27afXJKsVPHp+pnl+rVkzeWFnFfSuG2VrSW61qSU1+v32xpuB0ShmQHOTMxgZSiMxTNs68ocBvyOK7KrPTIQJxnzk/SVOHhnXuaV8yL7GpZjqDnNyzL+vupXwRBkIAnsVd/sCzrwNV+gCDYqUp5zcCyZgc/lT4nmxqC5FSDrKKjF3Qcksi+jipuWVeFKMDzF6OIgsD58UxpMGWaFl87OMiL3VHcDokjg4niibx0MmNZlYFYjlxBL61u1IdmD+Ye2t7AmdE0LWHPa1ozIIoCslA0ACwiCOCWRCwsBAQMy0SiOEgQ7dQ2j9NOTaoLuqkJuDBMOD+e5sGtdbNWW2a2yaXPEcpqc6r8Lh7Z1UQ0U2B78+wVhYd32G3UXOmZNVN/djSNZcFIQiGR1xZdADoXTkkkkdMYjCtQTOOr9bvIqAYZReeV3jgb6oLc0lVdEiYQRRGfS2R/ZxX7O6uIZ1XOj6fprPEX3dbtdmgJe3nLjgYyBZ0dTaFSkOZ2yvTHclgWDCcUwn4XTlks1TttqAvQEPJQ0A0e2d3E0cEEvZP2ak6mYOCUJV7tjfHwjkbeuaeJ7c0hhhN5ommVsNeJUxbZ2RLiwngGy4LnuyMomklBM8irBg5JIJXXGErk2VgfYEN9gGODCRySiGbY+b0zOyuZ8oFSMm/Y5qTYdWIzmZKW7ZnMUtBnG7C63TLuebqWlGKnnBiWNes6OzaYLKU/vdQT47b1NVc4w2usFrKXUQr7+sHBUjraD0+M8hv3bii99uSZCUwgrxk8eWacrmlBrUcWyReXrtfXXgqALcssrlpbOJdJlrkt7CWV13A7xCWlTgIlL7DJdIGzY2m2NS9e7v1q8Uiro0A5U7BNn3XTYryY8u2QBPxuBy4HTHmoCkBr2IfLKZIrmpZOpApzBj2SJOB1SnbtlG4rc97QFmZ9bYC37WwgrRj85NQIumEHIn3RHPvXVXNxMs14qmD3yfE8aUXnzg019EayfPeIXXL81p0NdNUGcDsk3rmnmaF4ni2Nl5fN1w0LlyxRKJqpxnMao0llwbYWc5HVlzYke9uORs6Opeio9l23g9ErkWPlFBFXI5YF83W7XodIe9iLy7l8Y9GzY/YYbSieJ61oq1LNeCEsuUUEQfgTQRAuAK2CIJyf+geMAIeW7QgBWRJxYZTqUWbidcLhvkmy+QIVXgdu0faKGE7kONg3SX80i6rp5FSdzQ0BBqM5Ylk7B3c4nqPG70Q3LG5ZFyY7LSc6nlWp8DrsGgKPjGGaJHMqzcXZ71ReYyiWwzQtAm4HN3WEaZjmuDjVMS+ERE6dVQezEFyyyPQQTMBOcRMwcWChFet4prKU8pqFx2l7+oTcMptqvHRPpnn67DjjyRxferGHQ71RuzYlU2A4nufieJruyTTD8SwvdUdIKxqZgt2evZMZJlIK/dEs4ylbLnFvexiXLNE7mWEgajtMa4Zt2FftdxL2OhhJ5OmPZIoCCRm2NgYRBTsNLrzAm6mgGyRzGpZlEcuqGDMKnEzLQtU1soXitWNBc8CBiEleM4mk8zx1Zpx/f6mXvmiWRK7AYDRL32SGZ8+Nc6AnSjxXoNrvYiiepXcyw4mhBC9cmKQ3kiZXsPP/eyJpxpI5TgzF+fKLveQ1Fa9TIuCS6ar2YVomTlHg4niKCo+D37hnHW/ZWodm6GxrCLC1KchbdzawszmExyGyqyXEdw8P8sLZMTRd46711cgiiCKsr/WSVzRGkjkm03m2NQZwyQI1QRcdYQ8ep0htwIXHKVLpdbC5PoDPJeGWRVoq3fzy7e2z2nFLa3lwsak+iMmllbJZ769188LFCSqLdUIz+fPvH+NDn39hznPWUeXDssAng89ZPrjc21ZJKlegeyLNA1vm9rh6/PgIY8m5FvXt1JTkZRzpnz0/cV2Ik6xmLMviX5/vYSieLvv7pvpLkxz1ofLByb0b69AMC9WwuGGG79O79zQjiwJBt4O37ixXymwLX+pL3dNqLip9bnyynU7XFZw9wLvcdXCgN8Kxwfisv//yHZ0EXRJ7WivZUr9wj6i0ojEYta9HSQTVsJAFi6665fWZWihtSzRWXW68TpnxaILTo2kaQ3YfklI0qv0ygWkDsfqgi/qQmw/ua6e1Qiae03CYBt86PFh6z3AiRyKncmwwQWNQRNd1vILGWErh66/08dknzpBVDbwOe6KrtdKNbph0VvsYSeTZ2hhiT2slyZxKQ8hFNFtgPJHnaH+cbEEnW9B5qTvKYMx+VjVWeLipIzyrJmfmM/r+LXUIWAgC1AZc+JzikqSvp/OJffOneF6OkNfBvs4qaq/CNsIw7eeoObNQeJXwyf3X3vfwekISBZzzxbeWSSJXQNV0Xu2N0DuRIZZVyRbssdtS2N1agdsh0hL2XNWK0UpzNSs9fwN8EXgauH/a3/OWZS1rFWe2oBO7TOxweDAze5tkgUcPj5T9Lex1cGYkSV43iaQLyJLISCKPZphU+Vz8+OQ4ec3inXua6J7Mcrg/TrXfyY1tYX73G8ewVxFzvPNzL/LD37ydP//hGRI5lQe31fPRW8pNMlXd5Muv9JPIaezrDHPLPB4tAC91R3m5J0qF18H797Uuql5hLKUgTOujpn7Mz7iuC+al16NZnWjWfsP/LMrCAhyfJjW7v70CzRIYS+RRDbOoiGenFjSGPDy4rZ6jgwlSeZXxVIGAW8ayoKXKyydu62A8VeD//OwCAvAb93aRVgx+cGyEdEHH45BQDYOcahB0O3BIIm6HxK6WClJ5bUHL8zlV58svD5Ap6LhkkYJu0lThKZnVgT3b+N1j42XbvTx06VpJFUye747ySl+MCxMZUopOKqcRz2kouoGAHVSKYrFWyrK9YizsVCvdtNvDJYtIokBONUp1DRvr/EQyKvGcWko9+LufXcDtkMgVjFIxryyCQxTxumVuaKlgLKnwn799sqSABrbpmG5Yc5qzidiDLd28dO4HY7ZaW23AxWS6QKF43rxOic/9vJu/f/+esn3MVEn68x+dLf38+Ikx/u+Mz/zgFw+jm3CwP8kv3FCP13spMHrosz/n9LgtUND1nx/n4l+8pWzbQwMJABIFi7NDKW7ZdOm++MyXD3KmuO27PvfirG3f8r+f48J4GpdD4unfuZ3qwKXPTSsaX35lgLxqcP+WulLR5hSf+OIBDvTG8Lpkvv+Z26idY7V2jStzZjTNn/7gDH/xw7Mc+qN7CRZXZP/4m8+U3jOaLC+w/aPvHCtdm19+dZA/e9eO0muv9MaI5zSSis6F8TRNlZcGjWcnLgldfOWFi/zxO3YDcPDoceLFjzgyUd7RZQo6X365n5xqcN/murIV57/+0Rn+5YU+BOD/eXgzH9zfXnrtM18+jKKbjJ6Z4IWLk9y56co1ObGMyu9/6xhpRedtuxpRizd/TodX+0e4vavlCntYfuRlSve7Wv7w64cZLp6+P/vhGVyKzhef7+bJM5Ey8Z3RVIGvvDLAqeEER4ft4LEnqfKfv3OSf3yml//80Cb+9YVeeiezWJbFRFEFsD9t0V+Ur47mDXb8yU9pCXsYjuWZyvZ5qTeG1B8nU9Co8Dh59sIk3z06TEuFh8FEHlmyU5d101bd++yTEl/6+E1sa6qY9X1e7I7wSk+MCq+DD+xrwymLfOrfDzKRsYPrH50a55XeGPs6qtg5h1faQvnN717kkf0bl7z91fDY0WH6oznW1fp52875/Q1Xij/64QDvv2P7Sh/GquH8WIrwPPPkOR1yiQK//c0TyCJ4HDI3rwvjccp0Vvt5ZFfjomt72qt8OCSRwVieg/1xbmy/en+0lWDJKz2WZWUsy+oDNluW1T/t37LL1ixlBWQuFN2gL5ZDN0zSBZ1kTrUVzUx7FiqaVdENk5FEviiBDZGMyvMXJpieNpnXdEYSeRI52//l/PjsoCtT0EupOpfqP+Zm6rMSOa1MznMl6Y1mGYnnSOQ1sgWdgm6WakomMwXSispkukAsaysepRSdlKLZ/jGTWU4Mx9ENE80wOTKQIJZVieVUCprBaDJPTjUoaCZjKaXU5qphEsmoV1TNATvtcGrG4vy4Pes8ksyXrfYsdEZDwJYpH00oxHMqec1eGZpSvCvo9iqVopvo0/6um/bPec0kWzDKVM/6Y7kyuWywA5O8apSpF+kmqIZJVtEZjOeIZAplAQ9ARjHmNRo0sZe4ZwquqbppBzzTFPs0w2Awni8p7U1x33/90YLaafoxT/FyX7ks9dnxSwPVOTLjyvjh6aGy31/tuzQDP9e2w8X7qKAZnBktv+eiGZW8at+lc91v3ZP2ceUKOmfHZ0tpr7EwpuocNNOiZ5r63g/Ozj8rNZyc/7VD/fFiLYbJ8xcm533fv79yaQLryyfmXukDiGYK5ErXQfn7XuqJYlkWpmXx83PlnzX9nvvWkYW5LAzGsyX1uLOj5Stfn//ZwIL2sdxs/OMfr8jnzuTn3eWraXZqrTFLbRTsvqlnslzJ0bRgPK1wejSFZRXT5fLzX0cW9rN6Zi9pWRbHh5OcHUtjmiY51SCpaOQ1A80wyWkGed3EMO2siFd7Y3PtvtSnJHKXZsp7J8uvL80weak3Omvb64Wp7zjzvlljdVIwrvCALWKYtp/jYDxPPKtiWhYjiUvPSMuyFlSTnsprpf7uer5GlhT0CILwr9N+PTE9vW1amtuyMbPwfSn4HAIb6gJ8+OZ2NtQH2dVcgd8llypVQm6Z27qqGYjleLknxqb6AI0Vbm5eV8VHb+ukapoHzdt2NrK9OcRt66vorPHxnjkUXsI+Jze2h2mscHNr1/yrPAC3dFXTVOHhxvbwgvxnprNcGZsi9qqDAHgcInVBNxa2Kk1blY9N9QG2NAaoCbi4Y30V25oquG9LLT63TH3IRZXPyf7OKna1hLhncy0f2NfG+lo/G+sDfPzWDvZ1hLljQzUb6wO8e08zu1sq2dIY5O27Gmmv8nLXxhpaKj3cvK6qTDVvPpoqPOxsCdFU6eG9N7XQEHJz18basnzmuoD7iu3jliHglrlrYy3ran00hNzUBJw4JQGPDGGfg44qH26HWLpWRAFaK12l9pJFW0XPWZxllQR4x64Gmis9eB0ismC/x+cU8TglXJK9nUOEkEvE55KpD7qpC3rwux1UTFNPkwRornBT6bWLpKfP4wqARxbwOUUCTrHsNVGkZOYrC+AQ7Bos3bRQZwRQX/udu8p+v9I6Y4XHXiCWRLhlffm1/dn3XpqJ29YwO9Vm+vn4zfvLZzR/7Y5Lq6WBOTIcP7i/Fb9bZntTiNs3lKc6tIa9bGsK0Vzp4cb22TOtH7+tg4Dbwa6WCu7YsJYmsVT8LhlZhLawm12tl2b6vvnxnfNu8yu3XRJ9CczIx/it+9dTF3TTXu3j03evm3cf//N920o//9l79877vpZKL9unroOO8pnI//zQJip9TmoCLv74LVvKXvNMy3l4/wKtCrY3hbily1ZGeu+N5as6//SRG+fZ6tryo1+/bUU+dyZ//9byVEW/S2ZzUwVzLUTJAjywrXxlzSkJvGt3I+/a3UxntY+bOsJ01c8vYlIfkHjbzgaCbqnUD0oC+JwSf/DAJu7ZXINuQlOlhx3NQap9TizLIugSqfDY2QaVPifv3DW3Gfmt057RU/WZ+9oqyr+HJPL2q1whuaX+qja/Ku7eWGs/R1dp/7h5cTZKr3vqgwsbK8oi1ATdvHV7A3dsqKHC6yipD0+kFf7p2R7++bneKxpH1wRc7ClaZNzcefkx7Wpmqeltfzzt55eBvwOmps4agVuu5qBmstgM0011fgzTYqBYUN4U9vB379tNMq9xY3sYt0Piu0eG+IdnunE7JBySSH3Iy12bajkxlMQwLQqaWTJwimQKvHl7PSMJhcYKN7/3wCa8TpnP3L3+ssdx2/qFXRgz07IWw9WqnImA2ynSVevn1+9ez/MXIyRyKqm8iqJZ1ARd3N5VTXXAzR3rq/nRyTFiWRVBEPjdBzZSH/IQSRdYX+fn4R3lHf4XP76v9HNHjZ9b5gn+vnVoiIFYjqF4ntawl7xqlAkkzIUgCCUHbICbO2e/RxIFtjcFODacnv0iUOVz0FltH9eu1gp+874NnBtL45AFHj82ikMS2VDvZySh8NiRIXoiOSTBvvn/7hdv4E++f4qBaK5YzC/SUe1jNKmgGSYv9sT4yM1tJPN2kWskoxLJKBwdTOLyOmiu9PIHb97EwT5brSzkkUnmdbY3h+idzDIQy9Fc6eGWrip+cnKMUyMpNtYH2Vjnp2BYFDSD0yMpDNOkPuRBNUyiWXu1QzcMQEDDrt/qqPbhccrkCna7Huwvn4U91DvOw5WXGlAELudU0lntpz+WxeeS0XSL6TFqVrVwSgKmZdFYObseqDboYjxVsAUhrPIR0PnYpdknhzS7a/rdBzfxuw/OrasligL3b5k/Jakx5OE9e1vwOG2lwLlqkda4MrIksq7GT7XfhaqbJdGWLx8cmXcbn8eNJNg1detnGOPuaq7kvz2yDY9Toso3/0P85GCKR3bZP08mZxvmTiGKQplU/nRu6qjmlT+6b87XNtaHOD2awiEtXMhAFEV+c5oogyQK9iSIBKNJhXW1r32h74X+8Su/6TXglcny55JTFnlwSx1//vgZ0jNW4P/7O7bzC/9/9s47Po7q3N/PzPaqVe+yJFvuvdvYgMFUEwIBEgIJpCek3dyb/BIuISE9JDeF5N70ShJCSCMETDXNgDHGvRfJ6r1t7zPz+2NUdqVddduymefzMWh35pw5c/bMmVPe9/uuKOHJQ+0Du7XZNiNdgRh5GSau6wtmPSfPzqKvPDuwm2MUIdr3IcNk5I715ZgNOp48pPbdBp3Iu1eXsWleHt944ihZNiM2k44bl5VysNmPXpTwRhRsJrh8bi4VuQ48EYnukJ/6niBLS1xk9k1w+t/Rze4QL57oYF6Bk5IsM9Sr1zeJ6jmnOgMUuCau3Pd628TSdfoiHG7xMCvXTmnWxK6/qCQjpQDRdOHY8NBtb2mybCaGeycmoxfAbFB9i7MdJnzhGNUdfrYdbWfzvHysJh3HWr0UZpip7Q4MtPdUCILAJbPPf3GhCU16FEVJ3P9fryjKexM+1wuC8CDw/UmVLIHxqpEcb/djFgf9WHzBKH/frTpGekMxNlTlcqDJQ7c/QjQmYTPqqMy1IqBwutNPNC5z6ewcJFlBJwqIwMFGN3VdAUozrTx7tI1bVpayp76Hg00eFpe4sJl07KzppjLXzsUTaBieYAyrSZ2AjYd4imCT40EG4nEZTyDKA9tOUN0ZQFHAbBQQFIFwLM7DvUFicYW/7W6kwGmmONOCO6hOfK5fXMSpTh9LS1xIssIzR9ro8ke4bG4eJSkGvaCaePUGo+Q5TIiCGjfBG45xrNWLUS8SlZQRB7BjpcMXoTfNhAegNxjjaKsXh0XP/EIHx1rd7KzpYX+TG5fVgCcYp7rDR3WHj2Z3GAXVnM0o0rfabaOxJ4CIjF4QaHaH6PJFBhRV/rSzntvXVmAx6BBFeP54O8FIDG9YIS7J/HlnA6WZZh4/2EKe3cS6mTm4rEaqO3z0BCM0u4Mcb/PS3BsiJit9AVIjZFp0eENxWrzqyky3P4pOJ+KyGlDHoOqEQhQFnCY9ayuzmZ1v598H2siyGllbmbwCbhaG+LeMpA8PGPWDSoBDRf1ePdk54NtwOMFHrJ82r+qMEYkrtPQGyE1wvA2FB6da4TQBIj3BGDaTbthkPy7JPHaghR5/hOuXFidJ0AK4+xzbQ31mldqkZ2IEInHqu4O0esL4wrGBnenmTnfaNEda3AOmn6faks0Sd9f18MLxDvSiKvU7Ky+1I36+Y/BVZRplQSQSl4jE5XHJBmfbTUiSglUvj8uyIBqXCcUkNbCwrPSZkQ4PGn22cDM9JKt9QwR8IjGZ371WRzQ23OT4WLOHN3NsA2aJoA6wOv0R3qzt5bWaLkQBLHpdkvla4oZ1kzfC1x8/SnWHD19EwmnSE4lLPHe0HUlRONXh53SnH6tRhzsYIRJTBQxUq0aFVm+YjbPzEASFf+9v6VORCw+LXP/Y/mYiMZnqdj+HE9pyRIaaTh8Oy+T6lYk2mycPtdITiHKk2cNdl86akIKbLCt4wzGcZsN5H4vlrcBQ4aZUxBUIReOc6vQT3NOELCu0eNSxzCO7G5lb4MAdjGLsW8x6KzAVktV+QRDK+/x7EAShkFT6xpMgHJMY7/pDOKFD7ArG+fueRuKy6pD4yqkudlR30ROIoSiquZLVpOf5Yx0cavYQi0t87Ymj3LKyjFtXldLpj9DYE8IXkTjV6ePV6k5uWVnKjupu4rLC6zVd2E2q8syePgevoTsViqJwvM2HQScOe7HvPN3N6zXdZFoN3NbnJDlWpuLlGpWhvjdZ1Soa6ot4HpH64spAIBqn1R0iEI3zoQ2VeENR3v/7N+n0Rbh+aRG3rZnBiTZ1krGnvjflpKfDG+Y7Tx+n2R1ifWUORS4z3QFVFWdOvhNRUGVJp4LeQJSRNuplBQJRiVNtXv7zrweISzICClFJndQoqPEfhk4rG90R/riznteqOwYEIXzROHriJL7S2zxhHGY9vlCU375Sh7cvtoROhN5QnK2HWgj3xUw63RmkwxfhK9cv4OnDbfhC8T6FPAmp349CUgiG43R4I0kvx5gCsbhMuzcyEANIFEQMOuj0R/nTzgYsepHyPDsf3FBBriN5QtDQ3YG6QTuY30hUdwRxB2MEoxIGIfnk1oRV+NGU0nac7GTJjMEAQa3uQTvhUIpCPHWolX/sbWJ2voP/umJ20sTneJuPh99oIBxT6+tjl8xKSrtpTi5v1vVQkmmdsCRxP+V3b017rO7+LWmPXQgoioIk9/1LiClytDX9YHvbkUE3T9+QoBJHWrw8c7gNnU7gkjl5aSc9zx9r5kOXzgOSJ8dD8Ufi/PmNegKR1IIW6XjxuCqd7Y4o7KrppDJ39HhQ4ZjEn3bW4wvHhy10PXe4nqr8s+90He2ZHsvh8pARQJs3xEM7qgcWIhP50xsNnOxMXpyaU2DnY5fOoqE7wBMHmjEZdFw+N7k3T+ohFIXDLR4CETVezsaqbLaf6qKhJ0goGqe6w09MBm9Y4m+7m8m2m9QFkIhEKCYPhKR45M0majr9zM63p1wYsRh0RGIyFqOOo0P8CkMxha37W1hacvYdvC19ZTUbdEx0vvLYgWbquoLMyrPztmkoZKCRTLs3xFi0AuMK9AZVf3V/RH0/6gUBnSBgMeiYUZrJouKM81qRbTxMhUvIF4HtgiD8QBCE7wKvAd+agnwHmIqBvSQpfU74UXoCESJxaaBz0OlE/GGJmKTQG4ziDqmrQK0e1blekhXMBh06EYx6Hf0+XzP7XtAzc+3MylNfksWZFswpYkccaPLw9OE2Hj/QQk1ncmfZ0KMO9nqDMXzh6bFS148AWIy6gU41FJMwiAKSovD4gVaOtHho94Z5dG8z2TYjLqsBQSDlqoGiKLR6wvQEoigK1PcEqO0K4jSr5l4f3zSTLYsLWVeZPSzdRBgtuFw/vrCELKvtIyap9yzLqsN+un20FncE35DBlyCo/mf9UY1Meh0vHu/gb3uaCPatcApAhtmACISjcpL4gScUY1+Dm5XlmegEMOgF4pJCjs1IscuMy2pAkqW0q4EKqmSuKKrmZTpBlQkOxyVCMQlfKMap9uE7Xz/cNjbH7X5CcQmdqK4MNnmSJzYt3sH2m2azZvB+w8kqX4mnp3pxP7ijjmOtXp442EKrJ1mswB2Mqjt7wSiNPcOFDLLtJq5eWDjmQbBGaswGHZk29Xm1mwfXzNIbnDHi3kMoGierL7i0O5D+zIMNg23loR2n057X7Y8Q6JtYNfaM3dk28Tl/tXpszui9wWhax97dDedGLOMbr45m8HJ26PUn7+hICghpRuMycGKIEMQnNpWwpiKbpw+30R2I0uoO84/dtWmvl2ExYjHoMYgCOlEgElcw6nW4rAasBj06YXADuysQQVaSZdBbPRGePtxGJC5RlmXl8nn5XLNwuHz0LStLuXphAe9YPtz3R2C4EubZ4vqlRVy9sIB3riodFv9srPT3m43nsZP6W4nQCLHREtELqkJsfoYZq1FHlsVAjsPIxtk53LCsmGsXFXLpnPPfbG2sTPoJVRTlCWAjcAJoBt6uKMrvJptvIvZJrvwbRMh3WbCb9MzKtTEj28qNy4rIc5jJtOgpzbSwsMiJ06RHFAR0gqrVv7EqF7NBx/yiDG5eWUJ5to1lpS6uXFBAtz/CNQsL+NglM7lmUSEbqnK469KZ3LKiJGWnIyWYoQ3dllw/M5vCDDMrZmSOW8hAP0Xb0GJfXi6zngyLHrtJh82oY3aenTl5du5YO4OqXDulmRay7CYWl2SokdT1IqIgUOSyYDbouGNdOR+7ZObA4LLZHaInEOXpw638+PlqeoNRLqrKobjP0XjT3FzynWYunp1DvtPM7HzHwNZ6/0rqT16sHjZRHAs5jtHr0qKHqxbkU55jpSjDwqw8GxW5Vlw2A/1juqEPSY7dyKISJ7lOEyadgCiAUQdOi57ybAvzCh3MzLUxr9BJrt1IhsVIhll1ADfoVbU2vU7AZBCxGkUMfSIHS0tdrK3Mork3iNNqxGEyML/Qzic2VbFpTj55ThMLil0YdarvgFEHGWaRQocBm0HErGegzPOLHJTlOLAadbgsBjJtqunQJSk6t99/JNkFL38UJcstC/Mx6AQWFDmYlZfso/HtG+YN/H3touFeufqE5vqBS2YnHbvnmkHn8svnDi9njsOEUSdiNxuG7aSW59hYVOxkTr6D1RVnf6X1rUJBhplbVpRwzzVzsJoGVwa//47UvlYAK8sGF0CG9lY3LivBpBfIsZm4NMVv3s8fP7pu4O+PX7Eg7XmlmVYWFDkpdllYOQ5J1cwEs6TPXjNvhDMHKXCaqcpTRU5WD7nW569MX8YzyaOfWHFOrjuUr79t8Nkuc+rJtZtYUpZDulf5jOzkTifHnoEsK2RajRh1Oox6kXnFyXWc2JauX1zIJVXZ6ATItBjoDUZxmHTkO8zMzLXzjhUlWIw6HCaRsiwbF1fl8MVr5mIzqgI0y8pczMi2km0zsXF2DhkWQ0oTMbtJz7xCJ1ajntm5yTvmRr3ALcsnJ1NeNcGuSyeqFhLGSUy6Lp2jvounq99Gxeibr28pCscQk6nAYSTXaWLL4kLeuaqUpWUulpZlcuOyEjZW5bJ8RiZzChyT9g0/n5gK8zYURakHfjEVeaViMrEHBMBu1hOIxBAEAYfZQFefvO3sPBsHm70cbPZQ3RmgMsdGkcuCoiism5k9MHgKxyRsJj3l2TY6/RF21HRR0xHg1tWlSb4DI/kJLC3NBASMOjEpujhASaaVW8eoGDSUyXRyAuqA3mIS0QkCBp2IPyKR5zCSn2GhPMeKJxDDYTEQlRXiikIkLrOuUlVYy7DqqcixoyjwqctUcyKdKKAT1Xo40OjmheMdyLJCTZcfh9mA1ajjwxdX8ocddeyt76W+O8hnr5jN6a4A2092snxG5kBQuA5vhFPtPlrcIXqDMT6xadaAcs5YGEuQtXAcjrb6KMm00uULE5cV8hxmChwyR1s9GPXqTtOMbCsZZj1N7jCiIHCk2cuGmdkcbPKSaTNQ2xXAE4zhCcawm/UsL8ti7cxs/rWvmdbeIP6IhF4UkWSZQFxCrxMozDAzK9fG3gY3CmA16fjqv4/S6A4hywo2ow5FEDje7mNXbQ+tnhAGUVBX2U3qzuOMbDu+sBqor8MTpicYw6AT6PBGWFqWgb9Pit1s0NHQE+TV6u6BXcl+vJ4uYFDxzCDq6N93SdW6XjrRRSimcLzdhz8cT1rx//f+QUfqnTVdw9IaxMEdIOOQ5/qFk4NmUPsahkvHfvHaeTxxqIXFxS5y7MkdfkmmlYVFGarZ5MzsYWk1pgZ/JM7eBjfesMSG2YNqiQ2dkRHSDP499In86UvVHG7xIggCzx5p44Zlw5UwAbp8g7t3spx+R0gU1QUYq1GPw5z8eotLMrvrezHoBJaXZSYtThn1apsXgC5vlIoxNKFAVGJ3vRtPKMqRluSdnWZ3kLkp4r2caXpbhj9z54Lnqwef3xZfHFFRWFDkZG+Dm+iQwLEmncDCIhsHmjwD7eOefx7ibUuL+MI1cwnGJOxGPcUuK9lWA919oSAS29KJDh/7mzyE4gpBX5Q2Xw8i0OGLIisy/rBEaaYVUJhX5CQWk3npZDvzC5wIIiwpzaAqz8nm+fn84LmTtLqb2FCVw80r0k9iblpewv3PVA+UQ1Hg1epOZuZPfHTelFoxe1Qe3tXA3vpeZmTb+Mzmqgnt9uTYTRRnWsgZ58Lr2aI2vXvuW5JQTBp11+KBdy+j2GXldFeABUVO3rOm/GwUbVozJZOeM403HB+T7WIqFKA3OLjV/sbpHsxGHdG4jEEEd5+JUjAaBUVhaVkmoiCwKUEZLBSVaHWH2H6qC0mWaewN8t615XhCsWEO0+nQiQIrZmQSiMT5+54mFAWuWVSAw2ygrivA9lOdFGVYuHxe3rg6rOAE/J36UVCHtv4BQ2v1pd/QG6bDF+Fgk4dcu1EVSxAEkBUiksLPX66hIMPMv/a14g5GMel1HGh009AT5NI5eZRmWfnV9hqePdKOjEJNhx+dKDIz18b6ymwURaG6009Nh5/jrV68oSieYJx2XxijXuR962ewujyH16q7eKO2B184TiAa56nDrdy+ZsaY788bjjOafp4CVHf6ONHmJSqrE8EaQwCTXsAXUm3dBAGaekK0iOAJxREE8IXjdAdMmAwi+xvcBPq2mgOxKPii1HQGef5YO6G4TCQq9U0hlAETi7ik0OGLIPSVE+DZIx2qT1FcdYiOxCU6/VGq2/0IAkTjCjERPN2q2IRRL1LXFcQTjieZgEpxmVZvhN7jHfSHffKFJTp9EZ461Mp71yRPsP95sIlNywaVCJt8g4ZmqTbQ+wMEhmMKh5vcrE1Q5Xv6cOvA313B4U7LoQQbtl+8dIovbBn0e3jy0KACWGdguG3cSyc7+MeeJg43e1kzxARyV103/9jbRFxWMBtE7r0ueaW9tivAK6c6Kcm0sGnO+J4xjUE6fRHeqO1hX4Ob/9xcRVbfAOn5401p0xxPEcesn52nuwbMNF440Z520vPtJ49w5SLVpKi2M72vWENPgJ++WE0oJtHmKeTmlYOD1tequ/jDznp0gsDHLpnJ8hmDE/0On9qmFeCVE+3D5K5TUdft55k+k6ihseT+sbuByxecfb+I3+2oP+vXTMWjb9YN/B1XQJQVonEZb2j4hDUiKfx9b2vSJOZkm4dH94KIQE1ngEA4TiweG5jwDOVkux93MJ6Uh4xqMv6tJ4+T6zBR3622w9PtPl473UNMUmPP6UWBnkCUqxeqARj3N7iRFYUd1d0jTnp+/Wpdsm+lpBCITM48feSIfunZUdNNb0CNnReNy5gmINTy05eqaeoJUZFr4763nZudSo2x0xOMjjq+ef9vdzG/KAOTXkQGyrOtOM0Gchwm1lXmpFXr236yk7ruAOsqs6maxCR+OnJeTHrEKRygROIyOp1AVJKQhnhbBqMSxS4zl8zO44Vj7WRa9OQ6LWTajCwodBCX1cj2sbisxg1I8Fvp8Ud58UQ7S0pczBqhkRxv8w0EATvaog7edtX10O2P0u2PsrTMdU5XWvo7cdUET1VzCcdkTDphICinToD/ff4k4ZiMLxSjMx7hkTcbWFDsZPvJTqrybLx8sguzUceJNh+KAnFZItNmZGVFFoIgkGM30uwOEYnL5DnM1PcEiUoygqLwvWdOYjedpjjTQpbViCgIOM0GzPr0HXlMkjnR5iPbbqQwQ50ij9X0T5aVAelTBZAkGU9M/VvsqxBPOOGF2ucY6AlFsRnFlLa1CtAdjGHUDfdVMehUfyGTTkCvExAEYcBBHEVBRp189Y+jQjEZi0HEpBeRlMGAp/2BU9MxPDargsOsp2uIHr8cH1lwYCQKXMnOj9LYzIwB6PQmv+Kto+zo/uylGto8YRp7ghxqdrMoYSW9X/hBkhW6U8QbeK26i2MtXhq6gywtzRzXjqHGIHFZQVbUYJ6d/tDApKepK/1gzywmC8sk5ZfQfpUR2rIoDB6r73WnPa/HH1V9BoGGIb5dTb2hgeDPbd4QibubOnHweZtTlGyymY7eQIxIXCIuK7iHBM68aKJ2SpNEdp+Tyw6jvjd5589i1FHT6U/rIxkfEmixNyQBCm/W9dDjV4Msd3nT7yb2BmIpfR37v+vwhYnEFQRBnQhF4zLRuKyq7ckKPYEYB5vc3LSimLIsK029QRaM0g56Uyzq2E3J/YqiKJxs92PQCVSeQXWsWbk2trtDzM13jFsBtp+mnhChmDQuXziNc0fGGNQpQ3GFvQ1uLAYRvU4gEpOIy1CVZ6fZHaI0y4LLmtxmfWFVkAvgtZouInE5aVx1vnNeTHriU6j/KQP+PkfXod1kMCbzh9cbePB1NZr2/U+f4BOXzuRDGyv5x97mgcGmLyLx3aePYzHq+Pnty/BFZD744C6ae8PkOEw89R8b6fRF+NTDe6nrClKaaaU0y8y8wgxuXlGKUS+iKMqAnn6WzcgLxzvItZt4rbqLPIcaFPVc0j+Oj/XthEUTXkpdgRgvn0p29g16IjR71FBNzx1Lnac7EOYjD+7idGeAUHwwv23HOzDpIFHYSQD2NbrJtOjpDsY52uplV203Vy/M5yv/PkpvMMrdV89lVr6DuCTz+b8d4FCzh/IcG199+0KKXRZCMQnXGO51iKDUwAQI+nY6UjQ/SVH/udON5vrzGpK3pIDU9650hyXc4YQXTILwQuIlFdS2OV6G5hGV4M3abrxDBmiR6Pjz7sc9ZOV1POucC2YkrzLVuUcOjtY/iI1KyrAtKH2wa0AO+6l9LfzwXcuSjj9zuJUdNd3YTHo+dXmyspuiKLx+upueQJSNs6anPft0RJeg3BcaoYse6RFp9AwOZI83daQ9r6ln8EHKcaR/2c/MteKPxOkNRrljbfKO5tYDjbxRq6qbXVSRCYsGd2IS51tv1vawZXHqIJWJlGdZ6elr/zsTzLkARPHcvFpN6ecFZxXvkEe5qSfI88c7U5+MuhuUiKTArrpesqx6Ovt2ll+pdadNHxxhwtzQmxh9HnbWDRd76AnG2H6qixMnq3lktyrssqe+mxuXlfCL7TUoKMzNdzIjx8aKGZl8/E97hpVZAbbtPcUdFw0GWX6zrocHd9SjEwU+ddmsM7Zq/n8v1gBwsMnDt25cNCHJ6fJsC6/V9LC01DXFpdM4E4SiMcaitzYwfojB/kYPNpOONk8QQRDZV9/DyvIscuxm8hwm/JE4Xf4IMUnGbNDjDcV57mg7OlHgznXlk1Y+nQ6cF5Oes8nQd/cvXqph+4k2DrUm6xOdbPMQk2DLj1/h+qWl1HQEkBQIdAf59J/30OWPcbBJtfM+0urlVIePPfVumnpDfOGauTjMekx9OxfuQJQcm5E2T4gjLTpOGwKUZVsp7ouaG43LtHvD5DlNA2nOR96oSy+nOnTioaC+oLoTVtN6gnHe+YvXqe3y47KY+PEL1dx73Txeq+5id10PHb4wvYEIb9Z1U7w0tZnMWx1PWOL//e1A0nfbJmERc8dP3+DgBGWav/rvat6/fs6E0v7HQ2/wwt1XDXy+8291A3+n2rd6tbobBdWU8LF9TdyxfjAYa7M7xBun1UHrVO4qX+h887Ej/O4jGwCYirH2KXf6Y4n5v+93h9Ke99yxTmq7/MRlhYffbGTLksHJy47awf7nBy/U8OkrU4sv/P71Br7y9tHlpjf+z0sDfw81xvzvfx3j3WtTREw+w7xw1q84NsJ9uyrj5emj6SdKZ4KP/XtQyTIch9++VsvjB1oIxeIsKMxgUYmLw029PHU4dRTR7c2xpODHR1u8dPU5tZ3q8J8VU6FZ9z41Ien8fc1u3IEIe1L4U44FRVF46nAb1R1+LpqVzYoZmqDMmSQQUxjbnvQgCuqiv79P2r3TH+Fkux9RFMgwG8mw6HFaDMwvdPLedSUcavJwvM2HrCgDoTPOd7RJzyjEYdiEB9QOUQGaPVH+vKseURSQ+nZDXjzeNewlGJUU/JE4cUmmxR1icYlr4FiLJ8ypDj+xuMTMXDsmg4gzwQn3X/uaaXaHKMgw8+4JCh5cKNR0BIjEFXRCjEyLgT+/0UC3P4I3HCMigRSOU51CllljkANNUyene26EeaHOPdy0ZCQSu+u23uTn2WkxqEFx4/K0deKdlkjn6tdPT5s7RKBv5/LkGe4HBuU+NC5UugMRfOEYUUmhsSeA0SAiS7YRJ3AP72rgznXliKLA2spsjrR40esElqTxn5guHGr0IivQE3RPKH04Jg/E6TvU5NEmPdMcWVEtaqJxBZ0InnAMBQVBEMi0Gcmxm7h0Th4ZVgN5DvMFYxKuTXomiMWgIxhTHf8VWeaS2Tm8WdeLb4hTeT8ikGs3UuiyUJ6TLM8ZiamB0fKcZt6+tIiSLCuOBHvNfh+FnhS+ChcqVj2kMJlGQcGkFyh2manItdEbjJFhMZJlNxOJB9GJAsrErbXeElwI6zWT+YmH7io6zQbevbqU3mCUmWMISqmhMqd0cvK8ZwJBHGwZpiFBns16gXCfTVK2bfKvvnwLtEzU81zjvGBVeRb7G3rp8kUIxWWq2/209o4cPuFIi5eYLGMSdcwtdPL5q+eiF9WB5HSmf9wyUW8Ci1HHvEIHNZ0BFmsmctMek06NaWU36dH1tc/lZS7ynWY+sKFyYLdy/czR5BKSicZl4rKM1Tg9pxfTs1TnAU6LnmBMDRSZYzfz/66ay+931PHyiQ46fBGGmhibjTpmFzj54IYKnAkTml9tr+GPr9fhi8hkWPT89zXzkiY8AFcvLOBIi4f5hePdzDx/ybSbCbqHGypF+gYtHf4Yx9t8XDQzhx01XRRnmPCFYuQ6TLxnXflZLu35RbYFWkc/bUycqxY5Jyu56yq2QvMY/W+HOqoHo3H+ubcZfyTO5nnajHmsXDHbda6LMAxJGjRPDA5R8si26Wn2qD44ldmjBKMaAz1vnTWotyx/391IbVefyE7fd8WZIyu2GnUiioJqcu2NcOX8fPT6sxcH5eCXNk8onUmvit9YDBM38b06RUBXjemHHrAadYiiQCgmsaDYySc2VdHqCZNlNXKs1Utlrn3Y7o43HKO+K0h5jnXYOBVUEYSHdzUQjEpcvbCAuQXTb8z61olINMXkOsyY9AJOsx5JUfj9a3UcbfHij8TJsZuwm3QDlWvUCVw+N48FRRm09zlbN/UGefxACztqevBGVHtndyjOm3XDo4FX5Ni4bnHRGVV/mW74QtGBl4yIGrwzkUAkRlySWTbDRZ7TjEGvY2V5JlcuKEiKG6MxnPD4LMNG5FwZOLX0Jt9EUB57V9Y0RJ2o2x/FF46jKFDXPdyUVSM1v3+j8VwXYRhv1A72n0PVtfonPAB7G9P7F46VWTnWSeehMb1pdg9u5QkCiCL4wzFG0glYWubiVLuP7z17ggdfr+N3r9eehZIO8vfdDRNKJyigEy4MSwCNkYkDvaE43YEYvohEXVcQfzhOjz/Cz16u4auPH+EPO+qID5Fj/eeeJrYda+efe5tT5tvpixCISCgK1HdPTxVAbdIzAXTAf15RxbLSTHIdJt6+tJhwXMakF8m2m1hcnMGda8vIcZjIsRm4dE4uG6tymZlnZ1ZfYNLnjrZT3eEn05Y8W45JUzgiPU8x62BugROHSd1eFUQ1zlG/BLVBhLIsGyvLMynKsLCgyMnS0kwWlbhYVZ6VcgVCY5CFxZmjnzRGPrVx4j5mxZOwJLvj4mQn8S9dM38w34zhfjlWw2BXt6EqL7kcLgvzCp3kO82sKtfs0MfK4pJzozD5iYvSx78pSpBVNQ9ZKHEYB0VgCpzpfbfG+nS8N0GlK3OIqtHcLE0QIxHrBOLGAFw86+w+jyUZyeW8an4BuQ4TeQ4TxS4LZVlWrl9azMzc1BNeE3Dz8hJCMWlAjDM0CXXMiXDjyon1yXaLEYNOJMMyvc3wNKYWAXBZDViNIl3+KN3+CL5wnNNdfiRZ4XCzh4a+xcBo3yRoaFyyfsqyrMwpcFCQYWZ52dSNM6aSc7IkLgiCDvglMA94QVGUe8/Wtc2iKo85VG4yEYMAOp1IscuE3WykvieIXgBPKIYkw6wCO029IUqzrKyuzOI/N8+moSfIU4faWFbmGgicuKoyh+OtPm5cXjwQxFRRFPY29NLuDdPmCVORa2fDzCzeqO3BYtSxae75sT1cnm2lMtvKCyfVCOACkG0zYDXp8YZiROIyZoNAKCIRkyHTosdlN9HpDSMKYNLrafdFklaVREEdPHzxurl4QxIry8P8+0ArMVmmLMtCIKwqjjitBvIcJrJsJkRR4MoFBVy5oGBYGU0TjFeQbdXhC0uc5XfVWaHAaeRjl8zkXwnfXTWJpY/bL0qefGyak82LJ4bvVqbiM1cmB8BbVebkzYb0e0dFTgMtXnW1/q5NVUnHqoozcFn0ROMyl8/LH5a2MMNEbVcIg04YJrspigJXLxzefjRG5paVYwsUbNZBOI3H//IyJ3v7fvNv3JA+IOKMhAnydYvy+clrLSnPu3N9BY8faiMWl7h2SJ9w3/XzuefRw+hEgf+5aWHaa/33jemPJfKu1eX8cnstrZ4wX752Hrf+bPDYtUvKx5THVPPMpy/mqh9vPyfXTmSoyEN5jo2v3zSbT/7j5Ljy+cGty7j30cM09ARpcYdxpwhuOhozso00d0cZbTlRAL5yw1J+92odpzr8vHNlMZ+7ah73XDefX71ympdPdOALxzHodLx9aQnfe3b4vfzXNXMQRYHVFdncvraMTm+Ed646875vBQ4jbT41bpxzgpYO91w7l8f2t/DOldPPV09jOGb9+BZWRAZ9YY0JccnsJh2hqMzjB1u5eXkxHf4IkiRz1YICHj/Qwt/2NKHXCXxpy3xuWFrMiXYfc9KoEOp1ItcuGhzDxiWZvQ1uzAYxSbzrXHKu7ICuAZoURfmgIAjPCoJQrChK6v2yKWaUsCoAxPoCkHa4Q3QKIWYXOGnzRujqS3uizc8Dzx6npy+8vN2oY3Gpi2MtvVS3u/nVK6cpybTQ6wsTk6Eix8KSUhcuq4njrV5eONbO4WYPDpMeSZK5cn4BLquRHMf5I0ld1x2kLmH7UkGN30Ng8KUUig2+/LqC8QH5aR1gcoiIghqPAdQXjlGn1vu3tx6nONOCLxRHVmQkSaG1N4Q3HEMvKjS7Rap1Io09IXIdJpb1rSj4I3GsBt1AjIKYPLFZS3fwwtVk6vVGhzmZvjGJyd3eui6uXTroHzHWCQ/AX3fWc8uq8oHPI014gIEJD8CO6g6uXDi44v/aqQ7cIbV9vXC8g68NSRuOycio0d/9oeThjywrbD3USpsnzPVL0+8iaCRzvN3HWsfI/g2QfsID0O0bdIw52ZZeba0+4dDp9vQN1mEx8I6lxfSGImxekLyAVJFrZ1GRE6NeINOR3jTtcF0L71wz+oTuX3sbqelS+8AfvnAq6dibtV2jpj8TTIcJDwxXtfOEYnxp6/gmPAD3/OMQzx/rmJRKXjjKqBMeUN9hj+yq57UatQ97ZFcjn9g0G4tRx/4GNzWdAQQUfKEITx5KPekutQ3uNF6/ZPRYT1NFW99zFIjKdPiiFLrGH0jydEsPXb4wp5q7YPG56wdDUYl2b4jynLeOOf9EiI60cp+CxF4zKqtiUTqdgMUoEIzGqe7w449IfOHqOQTCcWYXOHngOfWZjUsKze4Qcwud5DlH7/P72V3fy+t9z5NOEJhdMPHguaMhyQrhmITNNPK05lxNei4Ctvb9/RKwFDgrk57x4OvrKXc3Dh+M9U94AL779ImknaP+n7S/kT15pB2zXmTLwgIMBh3HWr009gbJsZlwh1T74IaeEGVZVkyGC8viMPFl1V9FcaDNl7xip6D6moTjEkQkOv0jrehJgESn3823nzzGXz+2nhePd7C/0U1JpoWbV5QgCMKEVWguZCLAD59Njh7rnkR+n/jLYWqXjm3FfyhvNo+sgjQS3/3XvqRJz29eOT3wd1MKAYzmhCCYzx1qZkWCGduRFg8/fO4kkbhMs3t62iFPRx5/dhdrZ00sRlM/9b2Dv9UfdjbwtRtGj4/zi1cOpD2mKDIvnuwgFJOpyncm7eB99A+71YUZ4L/+spen/vPSlHn8YV8PX3vX6GV/bE/TwN/9gXP7eaVOk81PpNkdQk4VQGsUnj2WPmDtWGn3jV1x4rljg5PVzkCMX24/zZbFBfQGo/jDMeKyQkN3kGZ36shUH//7fupWnr3JTioUeWIm8v/3qtqej7T6+cw1i6eySGPGE4py+6/eoDcY1XbfR2GyhijBOBBXCETj2Ew6sqxG6rqDPLK7Cb0ocMvKEt61upRgTCLLamBj1fgDdxv7BDx6AhH+vqeJQpeZW1eXJYl5TQVxSeaR3Y10eCOsrRzZ7PpcjbCzgf6wyF5gmIC9IAgfEQRhtyAIu6Xg5J1OzyRDJ9wywxtkTJI53q4GebKb9BQ5LWRZDWTZjPQGY1gMOuKyQlNv8svzoZ31fOD3b/LoXrVDUhSFHTVdPH24DX/kre3/owB1XQEONLr5+55GXj3VycFG94DdqUZqnj6SOrDeRDhX88rqIfOlznHszv11b1PS555glLgsA8pbShZ+sjw8MX/pSXNwhE2UPfVuInEZFIX9Db1Jx7oSdqFPdkxesOL1+okFcdQ4f5AVhbquIBlmHVajHotBR9cofcTRFi9bD7bS6jk3eub/3pt6F+pscKx1cvd+qs1Hb1Ct3wNN03vcdyEg9P3LtasWM4FIHG8ohjsY5XRngMIMC/dcO4+PXTprYAIzHpaVurh2USFzC504LQYCEYkO7wRWP0YhEJXo6BMJq+0auW8/Vzs9PqDfKNAJnBp6gqIov0T1+8FUWHXer9kb9QK5DhOhmMSlc3Kp6QwwO9+OzWQgy9rLk4fa8YajNPcGmJ3voN0b5tG9jfxiey1mg46G7gA3Li+hrjs4ED3eoHvrOcsKDA60BVQp8FPtPuq6gzT1BGj3hllTmc11SzQzpXRsqMwmfUz78TEn99yYYy4bX+iAJL50eXLbWFTkQkDAF46xckYmOyZZtrcKz/7nxefkukVAumFdptVIl18dNPnDybvFJtSdToBs6+TX++zA1L++NaYT+U4zS8tcvFHbxf4mD3oELKNYY/zvC6fwhGLsruvhrktnjsscaCq4Ysm52SEJxyS++Ogh2jxhVpVn8aN3Lxt3HnPybHT5IkQkhcIUgjRvZWRZ4YlDrTT2BNk0J2/0BGNAJ6h+OMUuE95wjJikkO8w0uGP9qmaxpKEoQKRON5wjMKMsZlPCoLAnAIH2XYjzx5px2HWM2MKwgUMJcNiYFmZi8aeIGsrRxY/OVeTnj3A5cBOYD3w83NUjrNGrt1Mrt1EUaaVNZXZfPjimQPH3qztxheJIUQF/vB6A5vmFnC8zUcopmDQicQlmcw+W2GnWY9eFIjLyrQPdnYmsBh1KIpCKCYjioAC62Zm8a0nj+ENSwSjEn/cWc/Fs8e/FftW4emjkzcZ6edE57nxf9o3CZeJvR0CNyZ8fv54G23eMIqi8Mju6SfDPF35/J+288/PTs68bSKMtI79j72Dv9+R1mQTs0SDpI7A5HeD3W/tjfa3BO9cWcL+Rjc7T/fiD8eJxGX0upHbzsl2HxkWA2/W9eB8w8CmuXksPYvBOpWgjxTGM2ccdyBGbVeAuCRzoMk9oTy2Hm0nLiuIApxo18IHJOIJxajpUE0cDk6wfhMR+v6jE6CmM4gvLCMIAtl2E7PzHRj1IrVdgQEBglDf2CoUlVhdkcVFs8a+8phjN3HbmokrvY6FS8c4ETxXk55/AA8JgvAK8FdFUS54O4GG3hAt7maybHp+91otMUnGZtSxpiKLJw+39+1eKOQ51NWN/Q29PHe0jYtm5rCmMour5qurN9l2E+9dN4NgVKJoAs6K5zvB6OAgW5LVet38g+2EYuqLKCbDqXYvLZpvRlpGciwfLzPPkcJzziTm+1mWZHviuCwTjauxsnxTGcToAmc6+sw5TYONu79POFNoLeXCZ2+Dm9equzjd6SPQJ+fZMspsN8tmJBKT8IdjPH24lYaeIA1z87hqQT4mvY5gMMjSb75EVFLYOGvqZd+7JROzpjzX0XFadGTZjHT4IszImlgMq3UVOZj0OiKSRGWOlfFLX1y4ZFgM/Pj5UyhAoWPyC94KqoJbXJYJeCK0eIb7qf3o+VPkOYzkO80IwPFWLwrw21d1qkqgIHDLilK2n+rgUJOXDKuB+QVOijLN+MMSp7sC6HXgNBt516pStiwqHBCaSsQbjvHskXaMenHgOUnF9pOdNPWG2FiVQ2mWlVdPddHQE+SiWdlj3kE6J5MeRVEiwM3n4trnkrgCnX61w1SASFzmhRMdSX4RJ1o9uINRdtR04zAbiEgSt65OniG7rEZcWly8AYYOboIxWbMHPkvUnKPliq5JuN786OXT/Oc18wY+B0NxdCIoCujFyZk9ld+9Ne2xuvvP/q7ImWT/uREoG5GH3uw810XQuIDQ95mQDw3SOBKz8uwEI3F2nu4hKivUdgUo6/BzMtvGopIM7n38BNE+2dJXq8eudjlW3vXLXRPqa3JsBrzhOJnWiQ2oRVFkdUUWvYEo8wqdE8pjRo6NP35oNc3uMGsrsyj95oSyuSD5j4f3DIwVW8chzjFZOnxRPKEYsqzQP9SKRSR8EQmLQeThXfV0+qPICnT5oxxs6qWhx0hvX5pIXCbXYeLpw20sLXVRmmJCfKjJQ2Nf0PD+52QoPYEoe+pVP83Xa7px9u2mArxW3T29Jz0XOoU2MOggUYFXQI3obDWIhOIKsqygEwUybSZCCWpTH1xfht2op8hlocUdYnYaPXSNQRL9fEQBcuzGtDryGsO50AbjozE0ZNrGOXn8dHst0bjEuspsnjlD130rTYjOJHX3b0lblytKHexpfOsqp41UNxrjZ1mpC4tBxwvH2znR5huYrIzEomIX1R0+8p1qHLkSlwWjXqQgQ/XtuXl5Cf/cpxpp2k1T7xM50b7krktnsfN0NxsnaBpu1KmxWNo84UnFZFkxI4sVExMEvaC5fVUxjx9qB1STtLOFQQSLQQ8oeENxlL7rC6KAKEBFto1gVJ0EiQLYzQaybCYQVD9ZnU5EJ4qUZlnISuOSUZJpYU+9gE4UBp6ToTjMerJsRnoCUUqzrNiMOnLsqg9n2Th2FgVFmYY2CkPIyclRysvLpySvQ82DOwCLis++3etUU1dXx9C6SbxHSH+fI9VFbyBKk1tVYDHqRAw6gUybcUBdrv/82q4AkqzgshjQ6YRRV4k6fWHavRHMBh2z8s6sDn9dXR2KPRd/JD4QmKt/8qkoqhCEgEBcUUBR1O/0IjOyrLR5I8QkGYtBxBOKI4pg0IkICOhE1cxOQHUClBWFuKygFwXMeh0Os54uf4RIXMaoFxFQ1ftkhb5OQU884eUpyQpZNiPdgSiSrFCebSMqybS4QygK5DqM+MISoJDnNGNP0KH3hGK0esLoRSGpHIoCogiS1G84qSoRZdmMFDjN7D9WjT5DtYFN1T4ON3tQgFm5dizG5BdzfXcAbziOUScwp2D4il5/uyrLsgyL7j3a89d/PNOipyQreeXGG4rR4gljM+kozRzeybV5w0TjMkUZloEV2n5kRSEUlTDoxFFVaPYdPTVQN0MxipCfYSEuK7R7willQwucZrXtGHU4zAYUBSJxCYtBh65vaz8al/GGY9iM+mH1O51J1d9oqEyXupku77j+cpj0AsZQT1LdpCrj0RZPUty2PKcJgygOvIfGei8D19WJzC4YffErLit0+sIYdSLZ9rE7z0ejUU50qmWryLFgN01shySxv7kQxiRTiVY36amrq8NnGLqEB7Pz7JgM58875UywZ88eRVGUlC/682LSs3LlSmX37t1TktfQVbDzfYV15cqVJNZNulW+VPeZeK4ZOJ5wzlhWC29cVsRj+1tQFDDrRRaXunj36jJuWJY+TkFivt+8YT63r60Y9ToTpWLuIpQb7h93OpdFjz8SH4hYnEjirlIqBNSBcWQS7gQWg4hBB96ESLr9Q/iybCu/vmMlVfkOOnxhNv3PSwSiY3fSEYAvv20+H7v5SgrvfACAy2bAb+8a/O0X3/c03oiapwicHtJ2En/DX79nCZsXlgx8rrpnK4nWhkPbXWLaPDPs+kr6Njc07dx7nyQcVxAEeOgDq1mfEDfgb7sb+NrjRwFYWJzBwx9Zl5T2sf3NnO4MIAoCH9hQnqRIMxRTYdVA3aTCahCJSnLK9tGP06wjx27i+iXFGA0ikZhMtt3IHevKAfj0w/to94Yx6kV+9d4VmI3nx6b70P5GY5DpUDfT5f0257+3EknoKHO23TdQN+nKONo7J1VfNJTbf/Ear9W6h+U9Ep/681729smbf+emxWwYYzySqarrof3N+T4mmUq0uklPQeU8zO/83rDvC50Gnvj0JeOawF9oCIKwR1GUlamOXViRMDUmTNE4VTV1gDVhNUHoG5UbxxFt1zFK5NzJksphbiyouwQTSytM8LqJKXSigE4Qko4Jgvp/gygM7BboBAFRGN+1BBi201E25B2feHy07G1DdnLGc+uZ49zoE/oKIwCGIU3HbNANHE8V8XmgzsTBvyeKKI6tdQiCgEE3+Fsm+gvpB8ojIE7Sj0hDo5/h677nhjR+yJNiLCY95gkE99b39ReCIGCYQCwSDY1zQbpxhkmvm/Q77kLm/FhePENciKsGqWy6x3KfL3wl+Zy/37WMm3+2D4ClpRmUZVp5/4ZCbvzpXgBq+vIMx2Q6A2FuXF6EzWDkygUjxwj48bsWc+9jR1hTkcX1y0rHfF8TIdNq5PIVRTx+oI28DB3NvTEcJh06EcJxmJVjQa/X0emLoAgKoahMZY6NH926jB+/WE2rO8KyEgd/ebOJbLuRAqcFs0Ekw2xkV10XoqDaafujEp3eMAUuE7PznFw8J5c/7qhld72bhUVOTAaR5t4wvYEIZqOea+bn0eaLIssKep1Ahz/K+9eX87vX62j3RPjDB1fQ1B3hvn8fJhyXuOeauTx5qIOILPPpTVVU5qqzhWy7iV/esZxvPnGUwgwTnlCMdm+MkiwLgUgMl8lAqz+MWacjGJNxB6PcdUkl71pRygcS6ukrNyf/9ru/dCVLvvoMkZjEG//vomH1etfF5fzylTqWlzpZNzPZDOzEN7cw6+6txIEnPrVqWFob0C9E+sznkq+b2Hbvvrx8WNo/fGAlX3rsGJfPzWVVRfJM7W1LiunyRajpDHB3gkhBP5vn5VOaaaUgw4x1Ersql8/N433rZ9Dtj3D/EwdpSyES+L1bFtPUE2RuoZN1M3OQZYX6nmCSOefd18zlpZOdLC91TSjom4ZGKvZNE5+eI98YLMfb5mbz+rbBY4nPeeKSyZwcKye61AfKZhT5zjsWUZSl4x0/3YsAnPr26O+x33xgHbP/eytRBS6fPTZpya9fP5/fvlZPZZ6VNRVjV1NLvI/tH1805nSj5amRGq1ukilyWRmqJeMywkMfWY9rgmIUbwXecuZtFxrTwaRiuqLVTXq0ukmPVjfpGa1u3spiDVq7SY9WN+nR6iY9Wt2kR6ub9GjmbRoaGhoaGhoaGhoab1m0SY+GhoaGhoaGhoaGxgWNNunR0NDQ0NDQ0NDQ0Lig0SY9GhoaGhoaGhoaGhoXNNqkR0NDQ0NDQ0NDQ0Pjgkab9GhoaGhoaGhoaGhoXNBokx4NDQ0NDQ0NDQ0NjQsabdKjoaGhoaGhoaGhoXFBM/HQ5NMIRVGo7QpgN+vJc5hTBsi70APjpQsKmAPDovbePB+uWL6Uj/5pPwBfqgJ3SQV/3NnATctK+NL1C7nnr7v56752JAUU4LObZ/GpzXMG8njlVCeRqMTH/riHOFBihz/dtZGbf/o6Rr2O5z9zERaLhY/98U1a3WEe/uBaHtxRw3e21VCZZeaFz18+kNeDO2qp7Qpw+5oZlGZZicsKe+t7Kc2yUpFj46mDrdR0+vnYJRXo9embrDsYpcsfoSLHjk4UkGVlxGCJJhHuunwW3b4ILx5rJ8tmYv2sHP6wo5ZwHDZUurhifiEvV3dRmmVm68FWvKE4t68u5fljndS5w2odW0W6gvJAvgKwIN+K2WRAkhUum5vPtuPtHGjyAoMP3eXzsghFFXbU9AIwK9dKVYGDZw+1EwGyTGDQ65HlOJ2hwXLrAYMAJpPI5vn5nG73s7fZl3T91WVO7tgwk1A0zsM76zjQ5CMOrCp18LdPXMyhZs9A3aR6NkY69vDr1Xz58RNcNjuHX7xvzbjSLr17K+6+v0e67ocd8MUvJh9/vaaDD/5+D5fOyean71k9LO0jbzRwot3Hf18zG4PBkHQsHI3zWk03s/LszMi2DUubSGLdDEUHfGBdCY/sa8YbTh3Y+Zq5WURkEUmWec+6Cl4+0c6LJzq5akEeV8wv4nRXgOp2L9uOdfDedaW8c1U5Lx7voLEnSGmmFbtFj6LAmopMXq/pwWgQuWxuPvsbeqjuDHDJ7DxyHaaB6zX2BNGJAkUuCzUdPjp8EeYXOukORJEVhQ5fmAKnhcpc+7Cy+sIx2jxhZmTbMOq1NbALgcS2ey7fe/3luPu6nIHv/JE4re4QV/xwe9K5dfdvGVO5O7xhDja5+cgf9iADn7kinw0zK1lZnjXsuiPltbu2mxPtfm5ZXoLBIHK6K0CGxUCO3TTs3LHc42TqebS+eCz4wjHeON3NohIX+U7zhPI42ebl3wdauHlFCeU5w/uKs0V9d4DqDj8bq3KnpG4uZFK9p/551zqWz8hKcbYGgKAoqV/c04mVK1cqI0We3VXbw2vVXYiCwA+3nUx73oX40KxcuZIFd32Hl0+FpyzPty3M5/HD7cO+76+/h96o57F9TeyqcycdF1AnSAA2g8Cq8ixeOtUNgMuixx2KD5z7hc0zuWvzXB7cUcu3njxOXJIpdln48CWVNPaEONDoxmkxcOnsXL7z9HEURWFtZTa/unNVyjKHohK/21FLJCazoMjJlQsKMBVWUXjnA5OtjvMWkwhRefA36ef9qwv41iduTaqbxGdjtEFD4vG/fGAta2dnTyitC9g/wet+58b5vGtNxcDnf+1t5Av/PIyiKCwucfH3u9Ynpf3OU8fZ29CLySDy41uX4bIaScdUthsRkBM+Z1j0xCSZYHTw2yvmZvNmvQd/JI5eJ2I26HBZ9OQ5zbT0hjHqRS6fm8erNd14Q1EWlbj41o2LyLIZOd7m5alDbQDMK3Dw2IEWegJRilwW8h0mdtX3gqJQmGHhE5tmMb/IOXBdSVb43Wu1+MJxynOs3LisZNT7GS0K+EiLDBdi/5vIdIiQPpYB/9lg1t1biSd8ztl2H7t2vclvX6vlG1uPjZo+VbllWeauP+3lmaPJ76Y1FVl85OJKLp+XzyXf3kq9Z+T8TrR5+cgfdhOXFVZXZHPLihLeqO1BJwq8d+0MMm3p+4ZEpqquh/Y3E8nn838/QH13kAyLgZ+/ZzmiOP4FjLXf3kYgHCfDYuTVuy8bd/qpoMcf5TN/3UckJrNiRiZfev/bJl03Fyojvaee+NRFLCx2ndXyTCcEQdijKMrKVMcuiKU9fyQGgHweTODOBEfqp27CA1DbE0z5vder7lR0+6PIKao68atwTKHFPbg9EYpJSecebFHfTLVdARRFQQFCcYlAOI43pP6e0bhMQ0+Q/ol5bzCWtszRuEw0rg4k/ZF42vPeSsRSTHgATvdMXXup7e6dcFr3JK57os2f9LmuJzDQTtzB6PBr9X0Xjcv4zmL7kId8jkky8pCHp9MbJS4pyApIkkxckpFkBXcwhoKCrCi0+8JIkppbIBwnGFXvwR8evJeuQHTgGfCEYsRkmWhMQpIVopI87LmQZIVgVBqWj4bGZEnVmmRFIRSVUhwZzsMPD588x+X0fXuXPwJAc4oJz1A6/RHifc+gJxQb6A8kWRn2njpf6H9nBiJx4kM7nTESjqkJz2UdBCLxpD5MY2J4Qlp/no4LYtKzrjKHpWUuLp6dm/acC3mFYPfXxn9vlyaszgPkO1Sjq3yniYc+vBZ7snUQ+TYdTqe6SnzHuhlcPDuPm5cXJ51zzzWD5m8P3LqYP39kDU6zDrNe4EfvXIo9YQHtZ3eoZlFfvGYOq8qzqMix8enLZvG2pcV8cEMFF83K4Z0rS/n81XO5ckEBC4sz+NaNC9PeT4bVwFULClhcksHlc/PHXA+z86zk2vQIqKZLefbBR0IPLCpykGnRk+8YrJCMMVpAmPUiJh1UZBpSPmgWEQwJBwQgy6obc9kBMi2pzxeBdywrYuOsLPTC4Pc6AX58W/Ju2dBn485R7q80Uz3BqIN3r5uVdOy5D84e+Ht9mZORGHrd0Z7RdeUuACx6gS+/fVHSsc9snsu6mTlU5tr4/juXDEv7sUtnsqYiizvWllOaaR3xOqORZR75N7LowGESsZt03Li0iOy+9lXoNHL76lIumpVDrs2AKEBVro37bljEVQsKWFTs5PL5+dywtJgrFxTwzRsWsWluHlcuKOArb5vPzStLWFuZzccuraSk7x6WlLpYVZ7F2sps3rG8mBuXFXPJ7Fw+c3kVG6tyed9F5Vy1oIB3LCtmWZkrqZxGvch1iwtZVJzBVQsLJlUnGtOD6fKeS1UOvU7kuiVF/PqO4Quwq5NfR7z73cPTG/Uin9g0i09uGuxzbCJsnpfH25cUAVCd4rqVWcm974ZZubxrZRkrZmRyzzVz2TArh6WlLjbNzaPIZRnT/QF8+rLBfmRR3piTjchEf79PXlbFqvIs7rp05oTNVP/jslnMKXDy2SvnjH7yGaI028oda8tZU5HFxy6emXRsurTt6cK8wtTv1/etK2F1hWbelo4Lwrztrcx0MKmYrmh1kx6tbtKj1U16NPO29GjtJj1a3aRHq5v0aHWTHq1u0nNGzdsEQRjf0vRgujxBEB4XBOF1QRDun2w5NDQ0NDQ0NDQ0NDQ0UjEV5m31giD8TBCEi8eZzgK8T1GUdcBqQRDGbpOkoaGhoaGhoaGhoaExRqZCsnoOcC3wMUEQfgY8BfxZUZS9IyVSFKU+4aMMnBGvteki33k2SLzXre8tZ8GCBcPu/+L7t9HgVp0+jUC/y/fXrq7iy0+fSjp3KPc9doh/7m1mVp6DRz9xUcrrApS6TLxy9+Zh6Q83d3Dd/76Z9N0zn76Yfx9s4pfbTyMrsGJGJr9932rs5sk3zUhcZvlXn6Gnz6lPJ6h+MzPzHCwvc3G4xUNtV4B1FVkEYzK13QFy7QYONfkGHNCN4qAggFUPVqOeruCgk6AAZFr09Ibi6HXgNOvxBOM4LAbesbSQxw+20emPJgkKiIBeJzCvyEEkJnOq3Y/VKFKYYaahJ0i/T7ndKGDUCfSEBj1TjXqB5aWZ2Ex6ugJhopEYxzoGBSNMegGDKBCKykh91zIZREIxGbMO/vThdawszxpRCvS/H97NwwdUhaRsK+z5cnoVtaFp95zu5aZf7gDgxsUF/PC2FWNO+41/beXXO0l7/PLvvUhNVxCdADXfHt+zfKCxl7/samR2gYP3X1Qx4rkjSVa/Y1kRl87J477HDtEbUh1+dUCi6++HNhYRkwxcXJXLZXPzEARhWD7feOIo/9rfzPIyF7+8I7Ui4dnCG47x4vEO7CY9l87JQycOL6/G+cN0eeclliMn4fv//d+tfL958PNEyzj0Gf3DB1azsSon5fOWSDAYZPm3XiYSl7lxWRH/3NcycOzx28tYtGjRCKmHk6hU98DtM7lh0dxxpZ8KWeav/fsI/z7YwsoZWfz8vStGT3CeoElWj8w/3qzjs/84MuI5FVkWgjGZ+YUOPnV5FS2eMHpR5LK5eW/JMAWTvmNFUQKKovxNUZTbgM1AIfCmIAjHBEH4vCAII+o/CoLwKeCkoig9Q77/iCAIuwVB2N3Z2TnZYgIj25tfaGz5Y13K++2f8MDghAdImvCk45E3mwhEJQ40uXnjtBr95+L7tw07rzHhGonc+JM3h333nt+8zm9eqyUmg6TAvoZefrG9ZtSyjIVOX2RgwgNq/nEFTrb72NvQw6FmL8GoxLbjneyp76XDG+ZAwoQHkiWfg3GSJjygHusJxVGAmATdgThxRVWae/jNJjqGTHhAneFHJYUDjV5OtPmRFPBFZE52DE54APxRJWnCAxCNK+xv7OWVU12cavMnTXgAInEFf9+Ep/9aoT5VnrCkTlyH8uHfv5T0uX/CA9CdWshvgPf87NWkz7f9ZsfA348ebBsx7fL7kttn4oQnFTVdamEkBe79x4GRTx7Cn3Y2cLorwNOH26jt9I+eIA3/3NfC/75wamDCA8kTHoBfv9LC8VYfTx1qo82bWinvL2824A3FePlkF81p1BLPFnvqejndGeBgk4fTk6gbjXPPdHnHjVSOxAnPVLKnvpcWz+jKlJ/52xHCcRkFeDRhwgPwtocaxn3dxDfCZx6a3Ltror/fI7sb8YZivHiigy7fue1PzhTTpW1PJ0ab8ADU9oTo9EfYVdfLL7af5lS7n2OtXk60+UZNeyEyFT49swRB+C9BEF4CngVOAPOAS4Es4LER0n4bmAF8cugxRVF+qSjKSkVRVubmpldl00iNQw/LikYOwjheXFZVwcygE6nKU5VDblpeNOb08wuHBzxbW5lFlnVQLsykF1hVnjnJkqpYDKmbt04UsBv19C9yWI0iJoOIKAhMyEEtBQKQYzMw0pqjXgCDbvAM0xgvbjbosRp1GHQiunEuyi8pHq7q8v7ZY4tLkYr3XJqs+jWvYPA3TlP9g2nXFI98wghcvWB8fUJ5jvosZFgM5DomFrwPwGYQqcgeWf3NrFd//2y7kQyLIeU5mX1xgiwGkTxH6nPOFv3BDI16kexxBmfUmF68lVfDLUYdrjTPWyJXLhiUWrMN6XRtkxwRjVN8c8rIsg32Jxnmc9ufaJw9csfw6hb7LFwsRh2z8x0IAuhFISnA9VuJqTBvex74C/CfiqLsG3LsbkEQUq7f9vkAGRRF+dwUlGFMXOgvhP6o1mUuHdvvvhqAG3/8EvtbAtT23Xvd/VtY/fVnWFjs5LcfWMfsu7eS59Tx6j1Xc9l3t3K6Bx65MSdl/ts/dzG/e72eq+YXktWnP/0fV84nGInz6P5W3r2mlK0HW9n2uctTpn/sU5fw6T/tYn+Tl4XFGcwrdPKpzao85gPPnsBq1LFhdi7zizKmpD6y7SZ+/fH1/O+2E1yxMI/THUHynGYunZ1HVFFwGAWeONTObavKaPOGOdnhY3lZJv/Y3Uhdd4D5hU5EnQ6rQceu2i6uW1KE1Wjkd69Vk2s3otfryDSb2LKkiN++UsPyMhe5TjO7G3pYWpzJ4rIsjjS72VXbjS8U5USbD5vJwG2rS9nf7OU9a0oJRBX+uruBDbNyMOoFOn0RXqnuRi/AFfPyictwtNXNy8c7yXaauHp+AfOKXdhNOhp6QuTajDy2r55XT/dyw5JiHGYjobhEnk3H4wfbWVbmoqogg8f2NXDx7DyuX6oGoMx3qm/nVaUG1q9PDuRZd/8Wlt63lWAETqZ4Zn5+5xw+9uAJblqUx9XzkiWr//WpS/jC3/ZS1xXkkbs2DEv7r1uLueEvzVw0w8p/Xbt02HVHMmU4eM9G3v+ng9y5bgYb5o59sg3wiU2z2DQ3l+IMy7hMJ8tdRjbMyaPXH0Enitz7toVkWg28fKKDP+2sI9Nq4MblpTy4o5YXT3SzrMTB7z60ji5fhCKXBbMh9SjomU9t5J8Hm9k8Lw+D4dwOUuYXOSnMMGPUi9hMU/FK0HirM/RZXrnyvrTHpuIau+/djM2ox2IcfdZx88oZ2E16XjjRxXdvVuXty+/eSq4V3vzy+MtTd/8WKu/eSnGGjlf+++pxpx+a10SYTv3JmeJCH79NhDe/toUbf/gc+9oH7XZEoNwFwbie/7h8JrMKXHT5o1Tk2KnKd+ALx9CJAo636OR40pLVgiB8TFGUn08g3d3Ae4Cuvq++pijKC6nO1SSr06PJFqZHq5v0aHWTHq1u0qNJVqdHazfp0eomPVrdpEerm/RodZOekSSrp2JZ7+OCIPxGUZRxCREoinI/oElVa2hoaGhoaGhoaGicUaZCumEvqnDBtwRBuKf/3xTke8Z5+lAzV37/xSnNc39DL55QNOWxaFymttNPPC4POzb/3q189qFBR/9wNE5tpx9ZlpFlhd5AFFke+67c0SYvH31wF03d3vHfRB+RSITfvnqaTq8qTHC01UOHNzTsvMSyjpeXDrZz8f3beOnoKT73yF4ONfekPC8YjeMORukNRBm6Oyn11U+qXctoXMYTHD4ff+VkB7tOd9Le52jeG4gSk0YvvzccIxiJ843HD/O3XXVE4hKeYIzqdh/Nvcl1E4jE8aQoczgm0RuI0BuI0uMLc6zVgyQr7KjupGOII+43nzjCy8fbGcrpTj91XQE8wRjuYJSHd9Wxv6GHLn+Yjr57CkTiBCJqvQ29t3g8zuX/8wLbD3ekvM8jzR5er04tIOL3+0f8rZ4+1MyvXq5OeQzg648f5kS7O+UxdzBKszu9I+5Iv1OHN8TRVk/atGMlFoux9hvP8tNtJ/jTG7UcbHRT0+Gjrsuv/t6hGHvquvnVS6f4x+46jjb38tzRVnbX9dDhDRGMxml1h4jEVYmDVO1TUdTv4mNocxoa4+HVo51c+f0XmSoBoInywV+9QlWanb9wTH2OEolGJfY39OINqX3aUP7vhZNUt7mTvtvwrafZ8K2nk77r8ofZfjJ1vzYZ6rsD+MPJQjYvn2jn6cMteEIxwrGhkiZjp/zurbztB5qjfiq0uklPfYebLQ+8xMM7VCGqcEyi0xemritAKBofeMbiksypdi9/2lnLn1+vYevBJh7aWcuje+p56nAr+xt6qO7w0uVLLUIFg+8saZRxaP94IxiN44/ERzw3EX8kTig68WdorEyFedt9Kb5WFEX52qQyTuBMmLe9erST9/xh18DnqTC9+NzfDvB6TRcuq4FHPrwWuyXZy+wL/zhIXVeABUVOvvy2BQPfJ5qEXDk7m5/esZrP/v0AbZ4wayqymJln53RngPIcKzcuK0nKM90W51TIli687xn8kThGncAnN1XxyO4GzAYdv3jPCmblOwCQZZn/+utBWj0hVpZn8f+umjOua6Qyhzl87yXY7YMO8R3eMI+82cCBJg8lmRbWz8zhygWDDvR/3d1Ic2+IOQUOrl1UOPD9ihUrueuBv+ILx9lQlcOqctWJ/z//so8nDrYgKTA7z8H1S4uIxGWy7UZuW12GXpd6LeBEm4+nDrfykxdP0f9+W1RoJ8dp4XCLF5tRxw/euZTlMzJpdof4x+5GDjZ5KMu2sqEqh8vm5tMbiPLnXfXsbXBjNejY3+hGURQEQcAdjGI26Pjnx9dRmmVn4Zefxt/XCXzvpkXcvKoMgL/tbuSBbacIRuPML3JypNmDOxRHAKry7OQ4TNy+pozTnQHqe4I4THrKsq3cvmbGgESlqbCKwjsfAIa3j7/vaeTeRw+jAO9eXcpXrl+YdLzi7q0DinRDf6uvP36Y37ymqtHPzLHy/Oc2JaWd/cUniUpq6u2f20RZzqAoQGN3kHsfO0wkLnHH2nKuXVyYlHb7SVVlL9XvVN3u4yN/3EMkLnHrqjI+dXlVyt9wLCTWDahOoA6zHqfZwOrKTFp6g7xR6x6myicKqijAslIXPcEoK2Zk8slNVTx+oIVmd4i5BQ6u6Wufzxxp42iLl8IMM+9aVTqqzO50QTNvS890MTeZDpLV1/1wG4fbBwdQOdvuG6ibQCTOQ2/UE4hIXDY3jyWlLgA+8Ps3Od7qRa8TuHFZCavKs9hQpfqXLvnqs3hCMUQBXvjcRsqzncPaWt39W3AHwlz941cJhOMsKcvkTx9cMyX386tXath2tINMq4Hv3bIUu1nPt588yq9eqUVRYHa+jRuXl3L7mrJx+0ok9jcXVeh56KNXTUmZLwQS62ZRHjz+Xxd2HzIelq9YQc8Vg8Ps960tJdtp4YkDrehEyLIaWVGexeZ5eWw71s5PX6ohJqUe74sC2E06KnPtfPX6BSwpHS4m9dShVo63+Sh2WXjnqtKU+eyo7uKN2h50IigKCILADUuLKRtF/Ke2K8C/97egE+Gdq0rJm4TYEIxs3jYVOz2vKIry1cR/wPYpyPeM8vDu01OeZ3W7KvfqDsZoHrJiL8syDX36v6e7AmnzeOlkN/5onLa+9NWdfpr6dhCaeobvsqTC65347k4iwag6S4/JCnvruwF1JeFQ8+Bqui8cp9WjlqumY3wSiO3tw3cwAHbWJ+fT5g0Tjsv4wnG84fhAfYC6it7i7quf3uQdgris4OtbmUs8drDZg6yArEBPMMrBJvV+uv1RAiOsNDT1BlEUSFzQq+4M0OoJEZdkYpLM7jp196PVHVLLHInjSyhzpz9CMCrhD8fp8IbxhWMoQKc/jIJav/sb1fL4E8ry6L5Bnde99b1Iskxckml1hwdWUxSgyx9BkhX21PcSlxXcwSi+SBx3MJZ21WXrgaakzy+f7EBWFBRF4c263mHnJ3abL1W7k45tOz64wtrYO7y9RhM63Verk3//U51+wjEJRYGjrcPbcH8ddvujBIesqu5vcg/srCS2z6mg/3eJxiVqO4N0+IbLkIPangKROA09ajtp90ToCoRp8fS3z8H66P+71RNO+yLS0Bgvrx49t7s7/SROeIbSE4gSiKjPauIzUd8dUFeTgzEkSUrqs/1hdcVaVmD7iS7ScborSKCvz6/rTP+eHS8n+97tvcEY7T713fzyyS5kRe0fWtxhQlGJbn9qK4+x8lrt2FfG32ocmvrNu/OaSCzZSuDZo614gzH8kRihqERb365NXXeQk+1+4iO8Z2RFzS8YkTjWmnoc1/+stnhCaXd7+s9p7g0TiEpIskKze/Rxa4s7hKwoxCSFdk/6vmMqmIpJT5KIgSAITuChKcj3jPKTOwZXgMYg+DIm3n9ROXkOM5fNzWNOgTPpmCiK3LS8mGKXhXelmSWDqpblshrZsrhQPXdlKZfOyaUww8ymuXlp0yXidDoH5I9txon/xGsrsjHqBObkO7jrsiqKXBaWlGayZeHgCnyG1cjb+sp66+qyceWfn58/7DuzXmTzguQV/tn5DuYVOllbmc3iEhcXzx6UK9aJApfOyaMww8wls5Prx6ATWFbmothlYV3loCLdf10xG5fFgMOs45LZuXx4YwVFLjNrKrPSSgyDGji1NMvK8hLHwHefu2I2Vy8opDzHxrxCJ+9arf62C4oymFPgYE1FFotKMthYpZa5MsfGouIMVpVncdncfNZWZlPssnLL8hJy7CYWl7q4ZoFaL5vmqGXWi/DL2xcPXPODGyqoyLVRnmNjy+ICNs7KQS8KZJh1XDU/n9n5Dj6woZzKXBsXzcphSYl6vX5Z06FsWZK8e3j3VfPId5pxWQ3cffXwnbtil7oKY9IJXLc0Oe1P3rUYnajujnz04sphaRcWqnVnN+q4bW1ykNANs3JYWuqiPMfGTcuHy1lvmJVDYYb6OzmHrKZuWVTEktJMilwWPrJx5OCj40EnQIZFx9wCB7MLnLx7dSlXLiggq0++XQScZh02A2RZ9awsz+SmFcVU5ti4fF4eJS4rl8xWn99L5gy220tmq/dy8ezct2SAOI0zw4b5g23sXLaqJ94zI+2xYpeFRcUZFGdaWF0xKKH/7tVlFGRY2Dwvj9Jste/q57K5eegEyLYZuGO92q/MzR1cDe7/e/mMLNZWZuOyGnn/ReVTdj+3riqjJNPCpjl5zMxVd7bvf8dCbEYdZr3ATctLWFicQVnWyCvao3Gh74ZOBq1ukrEYdegTDAR+ccdqVpRnsaoimwVFGVy3qLDPMiabd68uozzLMiyEhoAagD3TomdWvp0V5ZlctWD4uAzgkr5x6CWzc9MGsF4/K5sil5mrFhSwqDiDihwbi0tGV+NdXJJBeY6Vqnw7sxPCXpwJJmzeJgjCD4BPofat/cuuAmrMy98oivLpKSkhmnrbSEwXk4rpiFY36dHqJj1a3aTnTJm3XQhmcVq7SY9WN+nR6iY9Wt2kR6ub9JwR8zZFUf5LURQDsFtRFGPfP4OiKLapnPBoaGhoaGhoaGhoaGhMhklLViuKskYQBBHIhcGA9oqitEw2bw0NDQ0NDQ0NDQ0Njcky6UmPIAifBr4JuIF+LzwFGG7QPwmGmj/88YOree9vBtXXXvzcpeyu68Fq1LG2MptsuyltXv/e18hn/3aIWBpnrHTmFCOp4oymmNPYE6Sm08/C4gxyRijbeDnU7BnRNGSqMAMWi0g0rhCVFLKsOoJxhapcB3sbBx3HC51GWr2qM6eI6lcTSXCgu311KctnZLKjpotH97ZgMeo48rWrafeEuPufBwlHZXLtBh4/1I7LoueZz2zgAw/uRVEUfvO+lcQlmQ/8fjcK8MVr5rCuKg+TPrVT1tmqm/ONY/ddkVQ3I7Xl0Y6nauuTeU6mIu07lubzg1uTd7Zv/L/t7GvyIQC1KdIeafHQE4iyckbWlLabcpeZOvegqIndKCIKAt7IoBDD925ZzF/fbCQQiROTZNq8ERYXZ7B5Xj4mo8iV8wvYdrSdXXU9rCh18fuddaDAbWtmsHZmNnMLnDxxsJltR9tZWJRBUaaVhu4APcEYOTYD/qhEU0+QXIeJuQVOFhZn0OwOMb/QSZ7TzIFGN/5InOVlGTy6r5VQNM6715RhNU5FGLe3DpKs8NKJDsqzVX+7c8V0UG8bWg4xEGHVN55jxYxMfv7elaM+X/3l/uLf9vLQntakYwZRfbdEhqi9P/npjcwvGq7qNpTfv38V7/vdYHiIq+bl8MyxroHrXvH9l6jrDvCeNWXc9/ZFSWlXfPUZukNxCp1G3rXayAPb/CnLPR5G6ovHysf/tJvnjrazoMjBvz558YTy2PLAdo61+Vg+I4O/37VhQnlMBdUdPprdYZaVuaakbi4kqjv8NLtDLC114Q1Gxvyecpl1GHQiTquBDZXZBOIKt6woYU1ldsrzDzd72F3XQ3mOjfUzcy4ov9OpeKv9N7BCUZSTU5DXmEmc8ABs+t5LrKvMxmbS0RuM8Z616R0pP/PXg4wj5A0wss35UDZ/7ym2fe6agc9xSeax/c3EJIWGniB3rCsf38WnAWEgHBp8y7T71UFb4oQHGJjwAMiQNOEBeGhXI9WdAd6oVVXOAlGJTd97kbkFDl6rVhXi+hW+ekNxrv3RDnpCUQTgkw/tIxiTqO4IIANfe+I4X71BPyASoDE25n31uaTP5XdvnfALZWja8TwnU5l29j2Daf+5v50f3Jp8/r4mVZFGQZ0APZowMGjzhHn2iKokN9VxAhInPAD+6PC4PJ//20EEARIflVdrujndFaAsy0qzO8S/9rWgKAqP728mJqv38aNtJ+kNVmAz6vmfp0/gj0i8Wt1NscuCJxTDG45hNugIRuJIsoIoClTl2inMtFCZY6emM8DmeXm80Ke4t6u2m52n1edSEAQ+sGHqBCHeCnhCMfY1uDnY5OEjF1diNkyRQs44mC6LPEPL0eIOI/ujPHO0fUxl7H++h054AGJpQls9c6SNa3/8yqh5J054gIEJD0Dl3Vvpz/53rzcMm/R0h9R13VZvlAe2DVdqm0xfOpn0Tx5W+6/9TT5qOwJU5I1/0n2kTe0jd9dPrfrlePCEYjxxsBVFUWOyJTLZuj3f8YVjPHGwBUWBbn+E+t4whaMnA8AdlgCJzkCM2q4guXYjpzp8/OXD67AMUfJq6g3y192NnGjzke80Y9CJSaIi5ztTMX07BjRMQT6TRicK6EUByygvG90ZjolR7EzWGBcFYWA34ly8CKcbgkCSikiuzYirTzVt6C/jsg6qdLlsBhzmwXm61aTT6lMDAOs4VAorhsQMMOnFATWac9GeDH1qd0PbvkEnIIoCGWY9ep16VJcQm8igE9GLAjajiFGvQxBALwoD3+sEAYMgoBMFBEH9bDTosPXt4FgMOkx96QBc1kF1P6dZ2+UZL/2CRsaE9qSh0h+Haqy1MpHB7VTUuX18IXamJWlEOs8LDDq1/wJtrDQUvShOSd2IgoAggEWvQ0zx2jTpdQPX0YvCBfc7TMWb7RTwmiAITwADAtuKonxrCvJOy4ufvYRN33954PO+L13ByXYfZoM4TC56KK99/hLe/rPXaU3QA8+1QWcAihypB091928ZcYWqxAlNfaFFHvxIckBGURR456pSmntDVOaeO7OHyXD7HJETYSeyJNPuj7KkyEGjO8y6ymx++WrdwHlbFuXz1KF2RCDHpsNiNuINhugOgVWEH7x7GfkZFmrbevn2s9UUuyz89a6LCEbjFDjN+CISayuc/Mcjh1g1I5Nf3LmKrzx2GFmB79y8BID/fGQfOgE+esksZuWllze8AN5fZ4S6+7dgevAzSZ+HHh+rScFIacdSjvGkdaHa0KZKu/8r1zD33q2E4/DPjy8flvZb18/lnn8fp9hp5AfvTjZ9y7QZedeqUtzBGFUjtKd+jKgSlaPhMIlsnp/Pv/e1IqE6PC4rsxORRJq6vfSG1UHgY5/cwN/2NBKISAjAkVYvm+blsqY8B0FQZddXlWez83Q3F1fl8pvXTiPJ8J41MyjLsZJlN/N/ty3jhWMdLC7JINNmpL0vBlSGxUAkrtDYGyTPYaI400JFto1WT4TyHCtWo553riwlEIkzK8/OgiIn/r6gkRrjI8Ni5KoFBRS5zAODhrPNeJ6/s1mOyhwb82a4uHZRIR/YUDnmMr740fls+sXRpO/yHEYKbAbcUZmGvth1T39mI1lWI5++vGpY3h92wK8SQo88+emNAztC180z8r5LF3Dzz/Zh0cPBr2/hi/88wHPHOvifmxYzlKvm5vDc8S5uXFrA929dMaop8HiZaPrv3bSI7z57gpuWF5HnmtgY46Mbyvnz7kY+cg53eK1GPe9aVUqnLzLs3f5W3uUBVaL61lWltHsjVOXbWVScQfqIVclcWpWNjEKG2cjViwpocYfZsrgwpWtArsPEhzZWcKrdT0GGiao8R4ocz18mLFk9kIEg3Jfq+74gpVOCJlmdHk22MD1a3aRHq5v0aHWTHk2yOj1au0mPVjfp0eomPVrdpEerm/SMJFk9FeptUza50dDQ0NDQ0NDQ0NDQmGqmQr3tOVS/2iQURblysnlraGhoaGhoaGhoaGhMlqnw6flGwt8G4DLAmubcCTEeCdlHP76eZWWZSd8lpv18GXz841to7Any+MEWvvv0iVHz3FiVwwc3VHDpnOF27p5QjH/ubSImydywrJg8hzlFDmeO812WWSDFjLkPswjoRGJxGZ0oEpWSZXtMeoHjX79mwEl2KOd73Zwp1lVmnDHJ6vGkHXp8PGnvXAFfvWViaR94+0JuWDeo7tjtj/DBB3fjDka565KZZ73d5NoNdAdiKRUlV89wsWlePmaDDn84zvPHWtnfp0R36ewcfv+BNRxp8fChB9+kNxhDUBQikoIAyIr6bBlFWFji4ufvXUGew0wkLvHPvc28eqqLVneIBcUZ3HfdfPR9sqSvnuriTzvriUkyJVkWFhRmcOPy4nPmp3K+kNhuHnqbi4suuuiclGM6SFYPfX5y0nyfjrUVWRS5LBRkqO/TggwzN68oYf6Xn5nKYiZRd/8WLr17K3UJ371xz+X8a18zJr3ITStKcJgN3PePPTz4ZtvAOQadwBeunMOHLpk58F2bJ8RH/rCHdm8YBYUsm4kf3bqUOQVOGrqCXPK9F1GAUpd5WH8zkd9sOvzmZ4KpqJsLBX8gyvrvvUggIrGmYmpDKzhMOipybPQEo5gMOrKtRi6encsnNs3iSIuXF493UJpl5folRYiigCQr/PtAM009ITbNzWV/g5tnj7UzO8/BpzdX4TQb2H6yk/2NbhaXZKQcO58rJv0WUxTl5YR/2xRFuQconoKyTYjDzSPLLX63T2fuWKt3TBMegGhc5qUTnSmP1XcHcAdjBCISp9r9Kc/RSM9IHmVhGcIxGUlh2IQHIBJX6PJodT5e3jid/IxMpuM8V5PKB/dMPO0Xnzyc9HlHdRedvjAxSeapI21pUp05Ov2pJzwAexrceEIx9tb3EpcVDrcMemTvqlUl3t+o6abbHyUuyYTiCrKiyl/3ZxmT1fgO1X39U7snQpsnTHWHH28kzsl2H62ecFK+vcEodd0BmntCNLtDdPkHRV80RueOx93n5LrTeZFnPGXbWdvD0VYvTb1BmntDdPujZ3TC00/dkM8n2nwEoxK9wRj13UGApAkPqPGZ/rK7Mem7F0900B2I4AvH8Ibi+MIxnjqkym9/f9uxgWezaYik/VQwnduAxsTZerQNfziOoijsb3RPad7+iER9d5BARKKlN4Q3FGNPfS+BqMThZg9xWaG2K4A7FAPUxf66riBxWWF3fS97GtxEYjI1nX7qugIAHGxyI8kKh5rOnQR6KiY96REEoSjhX6kgCG8D5k1B2SbE4hLXiMc/X6b+f16hk7uvmTtqfresKMFkENk0J3UsmPIcG5lWVUq5Kn905SeNZEYSGbXowGIQ0YuqrPBQzHqRnAytzsfL+kpX0ufJrJ6dq5W3B9ZOPO3P7lyY9HnDrFw1HoFeZMuisUY+mDry7AZ0aR6EFWUuMiwGVpZnYdSLLC4ZVKa8aJbaJ62dmUOuw4ReJ2LVC4gC6MTBZ8ugg9kFdqryVRWe/AwThRlmqvLtuCwG5hY6KcwY3KFeU5lNtt1IZa6N0mwrJZkWcqcwoPJbgT+8zXVOrjudV8LHU7Z1M7NZWJRBWZaNkkwLOQ4TR7921RksnUr5kM9zChzYTXqy7caBgLN3ripIOkcvCtw+JC7gpjl55NrNZFiNuKx6nBYD1/T1LZ/dPG/g2SzLtkz5PUznNqAxcbbML8Bh1iMKAivLXVOat8Osozzbht2spyTTSobVyKryLGxGHYtKMjDqRSpzbQOhRVwWA5W5Nox6kVUzslhZnonFqKMq3zHwnCwpdWHQCSwpndqyTpapUG+rTfgYB2qBrymK8uqkMk5AU29Lj6bgkR6tbtKj1U16tLpJj6belh6t3aRHq5v0aHWTHq1u0qPVTXrOtHqbFrZbQ0NDQ0NDQ0NDQ2PaMhXmbXZBEL4rCMJuQRDeEAThK4IgnF1vfg0NDQ0NDQ0NDQ0NjTRMhXrbL4E24DZUM/IPAz8GPpIugSAIur5084AXFEW5dwrKMSJjVZyaSlOK/nxXlRr42yfOjoL3/247wU9fOk0oPtzx/zs3zKM7JBOXZT59+WzWf+s5Wr3RYWICdqOO21eX8t/XLUirihWNy5zq8PGXXQ0ICHztBtVP4sEdp2n3RvivzVXo9Xre96tXeKnGiwH43q1Lae7xs+14J+9dU8r+Zi/V7T5217tZWGBkT1MEowjXLsoHRN6zrpx8p5kuX4SYLCEgoNeJ5DnNFLvGZgs9ktmMDqjMtVDTGaK/thwGgTkFDvY1etEJkGEz8IWrZ/Pv/S0cbvaxqjyTQFTmRJsHRVGIygpGUSEqiwTCElJfPlY9LCt1caDJTSAGNj2E4uCy6LlkTg6nOvzUdQfJs5vIsRuo6Q4RicbwR6E0w0BVYQbLSjL49Su1BCIyBh2EJNU5Pc+moyOgXskIiDqwmUScRj1t/ijl2Tb8/gCNgcH7dFkFAlGFXLuZ5/9r44jqbYn19ui7ili2bFnSsbv+8AZPHe0i367njXuH29mPpGA12rPWf3zLDPjJXcnHjzR5+NK/D3HVvHw+uqlqWNql9z2NNyLx5H+sYW5hTtKx/vaa7zSTM4p/ykiqOCUuE/9zyxI++8h+WrzRlOdsmpODQRRxWvW8b30FGVYje+p6KHJZKMywUJplpabDz4Ov17JlYRH5GSa2Hmpl87x8ApE4/9rfzK2ryphflJG2jHFJ5mS7n2y7kXznyGtM9d0BYpIyLML5+UK63+J8MXs720wXtav+cqybMXxtNbGMy0ss7G0KJR1PV+5fvHyKh95o5P9dWcXblpaO6fqXAb/ty++BZ47xx12NfHLTTN6/QVVb84fjPPDcCeYUOrhlZdnYbi4BWVY42eHDaTZQNMb3UiKj9cXnE72BKM3uELPy7JgNugnlcbzVy8kOH5vn5V9QdTPVKIoyrG+0GWDXPVdgsxjPUammP1Mx6VmtKMqshM+fEwRhNFm0a4AmRVE+KAjCs4IgFCuK0jwFZUnJSAPfdUOOld+9dUoerrkJ+b7ZGJt0fmPl+9uq0x77wr+O4TCpHdGvXq7GF03tz+WPSvzi1Tp+8WrdsGPz793K0W9s4bmj7fzi5RoOt3jQiQLdwQgXz8rle8+cBKC+O8hPbl/BSzVeAGLA5/6yn/6a2NvgwShCtG+2sadJVYeKyvCvA+0A7KjpZtPcPFo8YXzhGAICGVYDS0td3La6jLxRBntHmj3kj3BcAk51Jr9sfTGF3Y1qmSVFVdb63N+PDBx/5lhqFT8GpjsqwTi8Vuse+OyPq//vDsX55/5B9R9fJERNd3IZGjwxGjxdPH+8a+C7WEL2/RMegGjfpcNBme6gOgg/1h4YVrLuoPpbN7rDbP7By0nHh7b5xOflxkdaqBsy6XnqqFqudn+cP752mvdeVJky7e2Pu6kbQbV3pOturYefDDn/nb98nUBUYn+jhzUzs1laljVwbOP923BH1Hq5+kdvDHuGtx1r50SbD6Ne5P0XlWM1Tqzra3JHePevdo14zosn1PoRBdh+ootFJS5aPSFkBa5aUMDNK0r44INv0u2PsPVgG/lOE55QjMf3t9IdiBCOSTx/rIPX7r487TW2n+rkQKP67N25vpyMPgfToZzu9PPY/hYANs/LZ1FJ+omUxvnPdFHuSizH6/UyOWmOAcMmPOkIhUJ8+yn1/fLpvxwccdKTeI0XEr5/4MXTAHz1ieMDk56PPbSHAw29CIJAltXI5fOThQpG4/XT3eyq7UEUBG5bU0auY+KiH1M1/jgXxCWZR3Y3EopKnGjzcdOKknHn0eEN842tR4lJCkeah6uMnq91cyY43OJlqOxOIAZX/PAldtyjhclMx1QEXggLgjAwthQEYSyC3BcBz/X9/RKwdArKMSFaz1C+Uy9EObWEYxMTsAjF+9NLRBJG4r5QnO7A4Mp3ICINTcp4p34xSSYmyUiyQlxSiMvq34oCkRQ7WUMZ/Yy3JsEJ/vapaHUHRj9pioj3yZYrqJKZiXj6G2YaInGpLw+FeDp96ClGUSAmy0QlGUVRr91flv72K8nywN/heJxoXzmjo7TvSKw/vUIshZz7wHkJ+fTXgYbGdOfvfx8+eQtNcu0wEEjdV4Wi6nOhKMqwfmUshPveg7Iy8rN4oSMpCrG+/mYs7+dURCR5oH8ORt+6dTkZAjGtnx+JqZj0fAXYIQjC9wRB+C7wOvDNUdJkA719f3uBYcuPgiB8pM9PaHdnZ7rV9bEx0urA0GPfeuf4t7fHe80zyfrKrLQy0AvzTWyam8/Fs3PZd99VpFCBBtRGsazEmfIeavu+u2JBPl+4uorlZZmsKMvkf9+9nI9vmsV1iwtZW5nNd29aDIA9YQH6/101h8vm5JDnMPGu5UWsnZlDlkVdcU/cjJ2Va2VBkYP/d/Uc7lhfwW1rynj/ReW8f0MFt64u5Yr5+ZRmjR7/dkGhc9RzzCl24C0JmwA64Jbl+eQ7VFnh4gyj+jeqLWf/v1RVmWUZvpsgArNyrGSYdAioktwusz4pvV6AGZkWLp7pGlHSOxGhL51afoF0hgVGEf71ifVJ3w39nR99V9HA31fNHl6HmWZx4Jqf37IobV5zki3MhjH0uqM9M9+4cQF5DhNXzc/nkjnJe3gHv3r1QF39x6UzhqXdPC+fleWZXLekEKc59a7IWPnCVbMxjNBzFmeYmZFlYXGJk/vetoDPbJ7NNYsK+OgllWyam8fMXDvfePsClpRl8p9XVPHV6xewbmYOX7puPvdcO58lZZncd/38EctwyZxcVpVnce2iwhHN9eYWOLhkTi7rZ2azdJpJh2pMPdNlJXw871xzimfp5puHp89yWrhyXh5Wo47bVo9s2pbq+jabjbXlmRhEgasWDIag+P4ti1lZnsXNK0p4x4qR803FRbNyWFORxZUL8idk3jZauc8XTHodb19azIoZmVyzcHy7Zf2UZlr5yMUzuWROLh+/dGbSsfO5bs4ElWnkzh/7xLkJiny+MBWS1V8DngBWoprL7UM1efv+CGm+D/xNUZSdgiDcA+xRFCVt5DFNsjo9mmxherS6SY9WN+nR6iY9k5Gsnmqm2yBIazfp0eomPVrdpEerm/RodZOekSSrp2Kn53ZFUXYpivJTRVF+rCjKK8BHR0mzB+g3WF8PvDkF5dDQ0NDQ0NDQ0NDQ0BjGVEx6/IIglPd/EAShEEa1yvkHsEwQhFeAZxRF6ZmCcmhoaGhoaGhoaGhoaAxjKtTbvghsFwTh70AcuBn4+kgJFEWJ9J03JkaSkJ0o2Sb47LWLuG3NoA/PZK4hwDDp56EU2kUy7DZ+975VfGPrUV443klhhomFRU5OdgQw6ARWlWexfmYOK2dksu14B9uOttHiCXPtwgI+duks4pLMtmPteMPxYZKOFwoiYxchsOhhToGDk+1+gjEFgwhfuX4Bt68tvyDrZir4wS0LR5QCTSdTnur4dEn7xBuNfPLRgwBYDXD068nHl37ladxhKWXaeFzmh8+fos0T4oMbK85ou9GJUJRhprF3UOpkRWkGdT0h1lVm8fLJDnwRGRHYODuH0iwrBxrcRGSZXJsJQQBBEMhzGAlGJcqzbWTbjJzs8OMw6fFF4jhNenzROHajHoMIf9/bTFRSmJNnRxaUPsdtAZfVyMryTNZUZHPRLNUBq9MX4cXjHbisBjbPy0cUx+pVpjFd5HWXfe1ZeoMxyrIsbP/8ZeesHIlM9Jkaz7vApBd4/fObWP6tF1IeL7NBwxh0V2xGHXvu3cz3nzvJgcZe9jd6iEqDb/f+3zbd/czIsiDJ8M5VJXz68tmjXm8q2s1UyJT395E5dgO7752Y+tetv3id420+blhWxFeuXzihPD73twNUd/j56MWV0+aZmi6s+eY2eoNRrltcOOoz9d13LOSdq4f7t77VmfROj6IoTwAbgRNAM/B2RVF+N9l8zzTdEfjVK6enLL+xeEa1+mWae4P83wvVPHe0nUhc4nRXkP0NPZzu9NPQE2TbsXYONXvYdryd+i4fO2q6aXWHeGR3EwB13UGOtfpo7g2xp753lCuen4xHsyUUh4NNvgFFspgMP32pekDpS2M49/7raNLnyQzwp0va/gkPQDCFAFP/hAfgHf+3PenYnoZedtf10NQb4h97zphyPgCSDC3uZG3HvY0efOEYzx5txxdR260M7K3v5ZnDbdR1B6jrDLC/0c2hJg9HWzz7bHF3AAEAAElEQVS8fKKT460+XjrRyTNHVTnu5/pkuZ891s6RZi8vnezk0f0t9ARjeMNx9jd7ONzspbYrSE2nnyMtHl452cWu2p4B1arddT00u0McafHS2Bs8o3VxIXOuFlsauoL09j0ADT1jk4KezoynF4/ElbQTHhjbhAcgEJX43fYaDjZ52NfgTprwALz9xy+nSalS3xOiNxjlz7saxnbBBKai3Uw0j/4+sss/MZm8E60e9jb0EozG+cfeifWju2u7eb2mi05fmN+8Vpt07K2+gPnbV0/T7osQlRSeODi67vAPt506C6U6/5gK8zYURalXFOUXiqL8SFGUQ1OR55lGAObkn91gfSKgE0XWzcoi06rqlRl0AnaDDqNexCCK5DvN2E165hU60ev0ZFgM6ESBihwbALkOExajDkGAkszJKcVcKFhNyc14fqETvW5KmvYFydIhsVoms4J2xzmKgTZjiADbrKyxPwsf3FCe9Lkix4bdpG56zy90TLZoo2LQJe+e9Iv8DVVhc5oN5DlNGHQiJr2I3aTHZtJjMerItpswG3Rk24wUuSyY9CJ5DjNGvUiuw4TVqCfHbmJGtg1RENAJ4DDpsRr1mHQiJr0Oq1FHrsNEtt2Izajq/ZVmWREEsJnUa2icX5TlDKpaplPnvJCZN7pg55jYNDcLs0GH3TRc6fE9a0aOP2MQBQQByrNtU1OYcTLZHZGJ7u2WZlgH4p8VZowcQy8dM3MdOPtijs0fg/rqW4nL5uSg79t5Hy3ANsDiUi0mWyomrd52NnAUVinZdz5AJoM61/18YsMMfvJq/YjpS5wiT3/2ChbepwrEfe8d8ynLc7K6PDvpvHA4zNyvPA/A5y+bwY9frCeswPwcgaNdaj19dEMZqyty+Z8n93O8W10Z+fXt82kLQJ7VRE8oypOHWpiVbeXxQ63YDCLZdjPv3ziTyjwHVqOO8hw70WiUrYfauWSOGoXdoANPOE6hy4JZr06C/JE4siRT3xNkfqEDnU4dmIRjEjFJxmE2sGjRInxb7p9kDafHhBrcMjEKSoEVojHwxZJj71wxy8Vz1W5yzeCyCOj0BlwWI2srsnj+ZBfdviCz8zPIspt542Q7zSHIt8DVi0vYNDePP+9sZG2Fk5JsJz2BCI/ta6LVE8CoSLT44f3rCynNyWJfXTfuaIwr5hdSnGmjNNNKTyDMy8c7mF+cwcVzCtCJAsuWLaP3qm+Meo8iDEhQ90caMqGuMuoYHnPJKYBBB5II7sHQRJj78khcX7ULIChgtkB/HFQdQ0OZDqIfUtepMKcoE8CcHB0dvRK9knpPxoTzCqxgMeq5c2Ml77uoClNhFYV3PgCkfkmOZFLw5S9v5Q9RVWf+wDjTjnZ8LGlnGODlrw8//sW/7+ON071sS2PS855f7eDdq0rYsnS4LL0/HMcTjlLssibVTSrevyyLnQ0ejnWn/hW/et08njncSESCz14xh9drevj3gUaumpPJ5iUVmA16rHKADz58nM9dNpc187I52uJl7Yxs2nxBHtpZz+VzcqgoyMRm1NHuiyCgYDao0uaSomAx6gnF4mSYjQgC+MJx7GYd/rCE3aQjGJWwGHVIskJjd5BwLE5ehgWTXiAUlVWJc72Aw2TEZtIlLRJ4wzFMenViNBRNvS09K1eupGvzV4FzW7aGriAP76rjU5eWY7WOLu1/NuivGwtQWWDjSFvytsumqixePJXs2vvS5y7BH4nznSePMDvPzm9ebxqWb1W2Ovi7bW0Fy2dksqQsizt/8zwvn0ruIU3Aifu3sLO2if946Ahuf5wIsPVTG4jLMm//yQ4A3r+ujDvWVVKRZ8MTjBKMShxp8ZBh0fGTF09xy/Lk/mNoe3/8kxeR4zDQ0BVmzczk8cVodQPn1rytwx3gZy+f5q5LKslzTWzC5gnG2F3Xw+XzRwoLPkoeoSjN7hDzCzOmzTM1Xajr9LLzdC+3rpmRVDeJ5Jvgm7cu57I5BW9Z8+SR1NvOi0mPJlmdHk22MD1a3aRHq5v0aHWTHm3Skx6t3aRHq5v0aHWTHq1u0qPVTXrOtGS1hoaGhoaGhoaGhobGtGUq1NvOOApwpMVDpzeEw2zgpp/vTDped/+WYSuMe760mS5fBElWqMp3YBji4/E/Ww/xk1caBtIDSLJClz9Cls047Px0fPlfB4nHZb5189K050TiEv/z9HGq8uy8q09NwxuOIUkKmbbJO0WcCwe/fJuIVZCp9UOhGdrDyU6nesCqA2+fBdBFlXZyHTbevaaCD/52J/4EG66KDJHiLDsXzcrngedPsaTEzkMf3cjX/32IN2p7uG5xMflOA48fbOPWVaVsWVLC88faeaOmi+uWFFOYYcZu0PGb1+sw6QQ2zc1nVr6DSCTylnd+HIoeqL5/y6iqOGM5drbTbrl7K0cmmPb06dNs/uUxVpU5eeTjG4cdf+N0F4caPXzokplTpt4mApvnZ/LS0d4ks8kMu47ZBolXe6HEDD9633reON2JxSCS77TitOo51epFFgQum5uHJIPFqKM3GGN+oYOopOANxchzmmnsCVDfE2R1eTan2n0cb/VSnGmhKt/BvkY3uXYDkZjMzFw7VpOBZneQ6nYfS8syyXOY2F3XSyAaZ36hk/wMC95wjLikkNXXL8UlmabeECa9SOEko81f6ASjMebdu5XbV5dy7/WLz1k5psLUaSrL0V+Gdm+YNd96PumcxPKleub+edd63vEz1fTsoxdXcOW8PEqy7fxpx2lePtXFwWbfQD6p0tfdv4WeQJTlX38uZRlLM0zEZZlWX2xYefrpCUTR6wTCUQmnxYDZMGj2OZLiZLs3jCcUY3b+yH6CU6FQ9tNnD/PdF+q5fKaT33x4eP82Fqrb3Pz+9QY+dHE55dnnzp8mHJMG+jdNvW1kpuI9ZRXBZTOwbIYLp8VEOBan1xdBUhSuWlyEosCyvvfFtqOtKIrIxjm5GESBuu4AS0oyiUkyL5/sZElJBka9DoNeJMNioCcQRRQEzAYRh3m4f9x46W8buQ4TgjAx073zwrytfO4iJf+OH9IbjBGNj03P5ZLZudR3BxAFgbcvLeI/Ng9KR/7ulZN8dWuyskXd/Vt4dF8TdV1Bilxm3rVquN3/UK554CWO9dkmz8qxsu1zm4adI8kKG+5/nlZvBIAPbyjngxtn8tfdjciKwnWLi5iVN3FBhdH8D85XRvJtuXV5MX/d14ysgCjAtYsK2Xm6my6/OrR0mXX8720r2HzJ+guybiaLALQ8+Jmkuhlp8DEeaenzIe1183P5vztWD3x+/mgbH/3TXmRFYWFRBs986/3ntN3oBOgXjLIYdMzJt9Plj5DnNLO2IotMmwl/JE5ploWfvlhDOKb6AtZ0+PGG4xh1Ii6rAV84RkxSyLAYqMy1UZFt44XjHfgicfKcZi6fncM/97cSjknMKXTwuSvmsL/RjaQobFlUSFW+gz/urOPpw23YjHo+fHEld918pWbelobEvviXNy/jypVF56Qc02HSM7QdGJ+4h+h130p57mgS0IkIQFWuhZOdY1Ome/ozG3nqUBs/en7sSlaJdXaq3cfWQ6009gTJtBopyDDz3nUzMOl1actbd/8W6rsD3Puvw0TjMu9cWcJNK0rTXm/oO3wiv1liWZ54zwwWLhy/XPTM/96KpKj+qqe+eW7aTTgm8aed9fjCcVaWZ7L54vWTrpsLFXNhFQVn6T1l1otkWPS0+9QxVqZFh91sRFYU5hU6qe0K0OYJo9cJzM13Mr/IidWowxOO0eWPsKAwg3etLiXPMTGBC1A3D/74uto2VszI5OLZuWnPPe/N26JxmUhMQpbHLmDZG4gSiklE4xKNvUEkeXBy98TBtpRp2vsmJu3eCGOZDNZ3D3a8Db2pO+GYJNMTHPR2f6O2hy6/ugOlKNDhTeWSrjGSM/+O2i76f05ZAX8kjjc0KKkQjkmc7PCd2QKex0z/ZY4zy0vVnUmfd57uRu573pvd517mN1EhNxqXCMUkgjGJmKRQ2x3EH1GfjqMtXiJxdSu1qSdEJC6jKBCXZTzBGIqiLrrEJJmeQIxWb5hQTEJRIBCJc6zNT0ySkRUFdyBKdaefeF+/1N8XNvYEiUsK/kicNo/WV42VfxxpPCfXna4723FpanodBahP865NRUdfO54o6lgAvKEYwaiELxwnGEknQzNIQ09wYIH2dNfZlX7/x8no6CeloP8nio9+e2eM/joGtP5mFM7mezwmyXjCg6OyYFQaeA8196oS7bKiEInJhOJx/JEYjb0hApE4/rBEXFbo9k+sXfYTSmwbkxg3nxfmbVk2I6sqsqjtDFDdOVxs/+e3L+NjD+1L+u76ZUUcbvQgo/Du1TPQJahY/OMTF6d8OWyel8fBJg/zCp1j2jr78a1L+fAf9wLwg1tSmzOYDTruurSS/3u+BoNe5Me3LaHYZafFHSImKSwtc416nQuJsQRxBfjClaV859nBgUN/OotB4G8fu4jr//dVeoIxKrItXDI7l5m5Vh7a2YCiCGycncvNy0v5+Bm6h/Odm5YW8n8PDn6ezOrZdEn780t1fOylsb2tD38tOe3/u7KK54520BuK8vmr53DHTydcrAkxO99OfacfRRSwGUQyLEZaPWFEAVaWZzEzz0E4JgEKt6wsIy7JtHhCrC3PJBSVaOgJct3iQrYeauVYq5c8u5lVFS521bkRBci2Glg3K5dZeTbMeh3VnX7WVGRxy8pSvvP0cbr9Ua5akM8NS4vYXe8mEpdY1tcvvWNZCaIg4LIYWD9GNarzmZFW78fDL+5cMxXFGTfpzLzOdTnsZj1feecS/uuvByaVb0mmhY+uL+ZLW6vHdP7yGZl4w2OPO1M+5POyMhfuUJSSTAuCAGVZtjGZpK+ryGbvrF66/VFuXZ1+l2coU7GTcd87lk8o3eJiJ8favCwpcU26DBMly2bkolk5NLuDrJ+Zk3RM2+VJZlaunTGGnpowIuCw6KjItrGw2MVjB9T4S5tm52E362nsCXLD8hJqOvw8fbiNyhwrcwsdVOTYKc20UdPlJxiNMyPLRtUkLJoAXNbBtrGuMmf0BGk4L8zbNPW29GgKHunR6iY9Wt2kR6ub9Fzo6m2TmfRo7SY9Wt2kR6ub9Gh1kx6tbtIzknnbebHTo6GhoaExfZgOuwkaGhoaGhrj4bzw6dHQ0NDQ0NDQ0NDQ0Jgo581Oz2jqTBM9P/G82cCzZ9hutM0d5tofb0dW4G8fWYUnovD4gRZsJj3vW19OnnN86hap5HUfePtCblinSmMPVfIZTdlnx44d3Pbv3nGVoR+HUeSBW5fjshl5/2/fwBeR2Dwvl1/duZpHdjXwyO5GlpW4sJh0LCvL5PJ5w6M2t7pDPLyrge5AhJ2ne1hcnMHXbljIrtoeTAYd4ahEQYaZhcUZAMiywgd/v4vG3hA3LS/hoqocFvfZJB9pmRrp4UScJh1GJLom5x8LgA4YixeKSLIcOCQrfA3FpBeIxIcfzLMb+cRls7hzfcWIUqA33beVPQn3N5IS2tBjv/zlVr51emJpX375Ze58yq/eA2oE9UQu+tZzNHujKdMm5n3bqiK+ddOyMV8X4JE3G2juDXH72hmjSlZnWQ30BNP7CThNOuxmAxtn5fDJy6uIywq/3F5DfXcAp9nAjcuL2Vvfy192NbKqIotf37kqbV5jpbYrwKl2H4tKMijMGJSWlmWFnbXdxCWFtZXZGPXaOteZ4vA0kdedDuptQ8sx1AJ/pDIu/srTeMMS1y7I4we3Luf1093YjHpWlWcm+doOfUb/9YmLWFrqGrUs0803ZLrIMq/4+rN0B2IUOIzs/OIV56wciUyXupnOTHaMowP0eoEsq57iTDunuwJYDSIzsqz897Xz6A3FEAWBtZXZHGv18tc3G+gNxbh8bh7XLCrEpNfR4g5xuNnDnAIHMUnhdKefpaUuTAaRX20/TYcvyqxcK8tmZLFyRuaE5aaHcrzNS2NPiOVlLjr9ERp7QqyYkTlimvNm0nM2OHkWrnHbr18fGDC957d7uLgqlx2nu7Cb1J/i81fPnfQ1PvPYYW5YN2PYw/A/T20bvXwTnPAA+KIy9z52mLwMA94+hZvnjqlKWd979gShmMTBJg/LSjM42ORhYXEG+UMmeb95rZZDTR721PcA0NQbYka2FQWBE21eijOt2E16CjPMZNtN/HJ7Da/VdCPJCj/fXkM4LlOYYSHXYUI+A+5q3jEo94yVseaUSrNwJDGkVBMegA5/lB+/UM21iwqSvi+/e2vSC2XPOCZ0F31lK699ZTBt4oRnNK76/nM889nBl2v/hAcgVRH6JzwA7/3Va/zxwxcNfF705acG/v7zmy3DJj2JDL3fA429/HOv6qAZGYMk/kgTHlDbiDci8diBFiwmHaGoxLZjHXhCMcwGkVZPmMPNHiQFnj/eQV2nl/LcicfFkGSFJw60EJcVmt0h3n9RxcCxY21e3jitPktmg47VFVkTvs75xEgDgTM1eEp86t736x38/kPrz8h1RmLuNDE7HKn+Rzr27SeO4A2rPeOTRzq4tbaH/Q1uQHVyHym8w4vHOyhwminIGHnhcOjzP504l2XrDqj9WptvcipbZ4rp/LudK/7+xjheuGmQACmu0OqN0epVx389QKc/yj2PHmTTXHW8IMkyTxxs5aUTHcT+P3vnHd9WdT7u52pLlveecezsvcgiA0jCCqMNqxQKpRQ6aCmli05aSgvtt/vX0pYOSlvoYJURRhghkED23tPx3pasLd3x+0Meki3ZsmMnTnKez8eJpHvPuUdH55573ve8Q9Goc/jJSbawcEwWr+6pw+WX2V/XDlp4Lmxo9+P2y7yxr4FWT4CdVSbq2wNk282UZiWdcrvdAZnX99ajaWFleas3iKaFIzf3hRB6TjOFadauEJbZySaSzAYkSUKvk8iym4f12l+7Yjm/Wze8D8UUq4G8JCM9Y/QkmQ34QgpGvYROp8Nq0kcleeukMymiQadDUVV0EhSkWahxBLCZDBh1EiaDrqtsaaYNnSShShpWo77jmNBmx8Nq1GM1nnqSsE6mjxr8FDItL3XQZXtqdEvSrexrSCyWTU8dU5rNhEEnIasaWfZTTxbciVEvkWU34Qko6HUSOgn0koTNpMegA0UJ7+JlWE/tvtdJ4fvL6Qv1SgCXbDYiSaBpkGwR0/1gGagQdWF5/BwSw8m1wH/OyJWHhgn50Yk8O8esTpL6Hb9GvYTV1PuZIhCcq4zLTxvS+iKj60pAVkRenYwkMykWI/qOtZnVpO963iRbDLj8MikWI6qm4fLLJFuMWAz6jmefhNmgx2zQYzMPzT1q1EtYjGGlYprNiDek4AsqpFj7nifOiuhtWVlZmsvY95ZVohSmGalxJB7CclxOMkFFJdliYE+NE4BUi56SzGiNU1WLB0dHDPGxOXYsRn3X+VlJBrwhDYNOIt1mwuELoWka+alWdDqJBqePVm8IHTC5w2wrpKj4Q0pYKEIiqKgY9Tp0PVZsO/YfwZCaM8jeGPlIgMWoIyirXbsbZoMORdUw6XUUpltp98v4gjKKqqHXSWQkmUi2GDlw+Bhy0uBDG56LSMCUwtSocTO1sLfw0Tl2+zp2usvur3F27Y71VbY0RUdycvTi6UCNsyv3U6yy3oBMQFZJTzIN2T1l1Etk2y3IqkqbJ4hOJ4EW/lxDwxNUMeklRmUmoWoael1YWJdVlfYOk4JkixGrUU+nNYCiarj8IYx6HXqdrvtzRaXNFyLbbu4yYevctZI75g59h2Bn7jiuEc6BZtBJUSH9I1G18Fxk0oev1de46e+3jUe837yv+hI5NibDhNVqjXmsZ5lah4cWjzzothdnmHE01FNaWppw2fOJiooK8otKqHV4cfexW66TiNqhL063UtXm6wida8QZkY/NqNcRUsJjvDQziYqW2EqP8bnJHGlwxdwx78nUwtSu39QITChMJSiraIRzWymqhsMXIi/FTLLFGDUOO8t34g+F85PIiopeJ8XNSN/fXJwIg733Ijlc305A0bAYdIzNTe6/wGlgKPrmXMIfUvAGFdJsJipPVqBPzsLhHzrrk86ngNbx2m42oNNJmA06ksx6WtxBVBVs5rCCOaSopNvMhBSFRleAFEtYyaaTJEx6CV9IBTQkJMwdSmlDnGdNUFaRJAmjPr7pm6JqUc+wzvcmgw414vX2bds0TdNiar/PCqHHnD9WO5MZ0ueNzuDwyVbaImbNvnwVDECqGVr6MRPKSjIyvzwzKlnqlFwbz96zmHue3oE7IDMuN5kJ+ck0tgfISjbzifmjouromc35fMOsh5Aa/aBMMkr87KaZfGTFkvO6b+KhA2qevC9upuv+/OH6On42lt1T7eCep7ejqBpLxmbziy9cd1rHzcQsPSaLnWSLAU2DQ3XtYRM6CUZl2PjcxeXcOKcEgC//ZydbKlpRVJXpRWm4AzITcpP456ZqVE0jxWJk63dXsKWilXcPNrLxeCsaGtnJZvJSLKTZTMwoSePi8Tm8tqeOg/UurCY9d1xYitnQWwP3j40naXYFyEkxc8u8UXGzxw/U57KTwfymgx0HiR5LAvYl0P5p31tNhMUlWW89KELIxmHs5Olc/70/8a8dTf2fPIJ45YuLeHN/A6v31OHyhWhwhR/qfeWbq3h0JYcbXDz44j6q2zy0+2SSrUa+cdl4rp5R2Ov8ePfUQIgcv7eZ4KGHTq2OkWJGNhR9c65Q7/Rx8582EZQVJheksvYndxK6+sen5doSYYVEpFm9TgKLQUdRho2KZg9BRUMCbEYdVpMBRQsL/D45rDQrSrdyyfhcPrW4lJzkaBPUfbVO1uxrQJLgullFFGfYerXBH1L464YTBEIqkwtSWDo+myc2VOALKpRnJ1Hr9OMLKkzIS+bKaQVxQ1YLO6AEaUtETdSBTP8CD3RkH/bJUZ8db/ESUjW8wbD07vSFcHfsILkGkGTtfCGo0Mt3R1Y0ml1DEGngHGUAQ/m8oNHlR+kYRC392AMPBw6Pgqxo+EIKvpCCXwlrljUNfCEFR4QPkcMbRNM0grKGNyATUjR8itrVfn9HOnWXP4SiaQQ7jvlDCp6OOaVrPunIqO0PKYTiOIl1zjluvxzz+LlIogn/2kem68OIRFE1mkNDp5E+XXQmNvUFZbQIMac/VXGrO5yhPmyhoKFpYX+708GLYlyekzh9oa6dTYcv1DXnnw40eq+ztI4/t1/uOqYRtlQIKWp47Ksamhoe/4GQQlBR8MbY6XV1PF80rft1TwJyuM7O80OK1pG0GxzeUNfrzudaPM4KI+9Uy9BEeoDwlvVARIefXj+NmjYvv7l5JvN+/HZCZb531SQ+tWh0lObEYtQhAZeOS2ftMQeqBt+5ciKjsuy8f6S5ayG6/+GwJuOuJWVsO9nGyml5pFiMHKhzMSFvZGw5jxQMEnx+aRlvHGikxuFFVjSMeh3XzijkutlF3HWmGzhC+dttM7nsye73p6I9G8qyA8kmP5Rll03MY0eVk8oWD/ctH8eT30iomoSYmGvHE5SpbPMDYY2ZQYJQxAPk/pXTaHEHuoJ6HG1w89q+Ogw6ievmFPGRCO3wfcvH8tSmKnKSTYzLTSakqKTZTOglHRuOtnDLvPCO0IKyLCRJYkxOEvXOIKOzbOSnWmnzBrmgI5jB8om5bD/ZRnGGrSuQSk+unlbAwXoXE/N7zz2R/TiQ/o8kBWjveP2XFfEd0BO9VuSxjD782Zdmxj+W6JiOvFbK0LmCnZOkWI3cOKuMt/e2DkjpUpJhpLI1/MROMUD7IGTv/GSocyV2bs+xNXtUOiFFIzfFzPojzeyrdVLf7mdReRZvH+q9a9U5duaXZ3JNcwGVzR5q2/2kWY18auHoXufHK38q7DpHd0PO510egPF5Kdw2fxS7qp3ctXg0d/zRyODDTiWGAUi2Gsi0m5lWlMpb+xsIyApFGTbyUiwEZI1PLSrlg6MtrN5dS2lmEnlpFpLNRrLtJo40uXH7ZSxGPfPLMpg1KiNmEINZJekE5bAZdrx1bqrVyKWT8qh1+JhTmo7dbOCKKflUtnqZPSqdJleAqlYvs/qJ3nZWmLfNmTNHE2YDsRFZeeMj+iY+om/iI/omPqJv4nMm+qYv4W8kIcZNfETfxEf0TXxE38RHkiRh3iYQCAQCgUAgEAjOT4TQIxAIBAKBQCAQCM5phNAjEAgEAoFAIBAIzmmE0CMQCAQCgUAgEAjOaYTQIxAIBAKBQCAQCM5phNAjEAgEAoFAIBAIzmmE0CMQCAQCgUAgEAjOaYTQIxAIBAKBQCAQCM5phNAjEAgEAoFAIBAIzmkGJPRIknStJElf6nidLElS0vA0SyAQCAQCgUAgEAiGhoSFHkmSHgduAu7r+KgceH4Y2iQQCAQCgUAgEAgEQ4ZhAOcu1TRtvCRJBwA0TdspSVLZMLVLIBAIBAKBQCAQCIaEgZi3tUqSVAhoAJIkzQeUYWmVQCAQCAQCgUAgEAwRA9np+RzwHFAiSdIWoBC4bVhaJRAIBAKBQCAQCARDRMJCT4c524XAhI5yhzRN8w9bywQCgUAgEAgEAoFgCEhY6JEkKRn4MjAH8AJvSpL0N03ThImbQCAQCAQCgUAgGLEMxLztX0AD8CvACnyasAD0uaFvlkAgEAgEAoFAIBAMDQMReqZomnZV5xtJkl4Djg59kwQCgUAgEAgEAoFg6BhI9Lb3JUmaGvG+BNg1xO0RCAQCgUAgEAgEgiFlIDs9+cBGSZJ2ACFgFrBPkqQ1nSdomnbpELdPIBAIBAKBQCAQCE6JgQg9Pxy2VggEAoFAIBAIBALBMDEQoed+4GngJU3TfMPUHoFAIBAIBAKBQCAYUgbi0/P/gCXALkmSnpIk6SpJkgYiNAkEAoFAIBAIBALBaSdhoUfTtLc0TbtH07RxhAWgm4E6SZIelyRp7rC1UCAQCAQCgUAgEAhOgQHt1EiSNAu4BrgKaAe+A7QCv5Akaa2mad8d+iYKBAKBQCAQCAQCweBJWOiRJKkKqAX+DVyjaVptxLEXOo4lJPRIkqQHHgcmAu9omvadgTRaIBAIBAKBQCAQCBJlID4939c0bZ6mab/sFHgkSboIQNM0GZg2gLquAKo1TVsIzJUkqXAAZQUCgUAgEAgEAoEgYQYi9Hwj8o0kSanAU53vNU2rH0BdFwJvdrx+F5gxgLICgUAgEAgEAoFAkDD9mrdJkvQL4IuATpKkUMShIPCXQV43E2jreN0OpMa47t3A3QDFxSWUPrB6kJcaGLcvLOHJDyq73lc8urLrdWQbIj+vbPGyraKVP75/jFUzC7l76RgAHnxxL+8faeKxm6czoTBjUO052uBizYF6rpyST2mWvdfxPTXO09Y3iWCUIKR1vzcDgY7XBkCOODfHbuTmuSX8cd0x/Ep0PWlWPfNGZ6CTJHZUOsiwm/jbHXOpcfj58er9FKVbmVeWRUGahbmjM7EY9TS7AzS2Bxiba8eo1424vhkpvH3vnKi+iRzLncQb6/0dG6llv/LPTTy3tzlu2S89vZ399e387ZPzTvu4mV5oZ2+Nmx63ACbgqpkF6HQSJWlm3jrYxNicJD481oaiaXz3momkmE0canDhDypsO9HMyVYvDp9CabqZk44AdpMeX0ilJN3KJxeV4Qsp7KttpyDVysSCZN7e10Btu48phal8dGYhu6qcNLoCjM9LwaCDJLMBnSSh00mMy03uc9z01f+f+PNGqtu8rL5nPjabLaEygz024/uv4/QrrL53LpMKsmOWG+jYikdkuayES51/+IIKqx5bz/ZK57DU//p9i7n8V++fcj0Vj66M+k1PPHIlx5rcmA16ijNs/GndUf76QQV1zkDc8p28ua+eqjYft84twWTSR513/392sOlEK3+9Y3a/c3EiDHb8RuLyh6hs9VKamUSSeXCBeR3eIDUOH+XZdixGff8FYvDEhhOsO9TIwx+ZPCR9cy6xZn89Hx5t5nNLx6Bp2qCeUxkWHTNKM5hWmIovqGHQwTUzChmfnzIMLR559DuyNU27H7hfkqRNmqbNG6LruoDkjtcpwJEY132csN8P5vyxWv4QXbg/IgUegMXfXs37P1oZd3BVNHt4YUcNv3n7CBrwyGuHuGR8FgfqXTz54UkArnlsI4d/dOWg2nPP09txB2Re39PAS19cNKg6TieRAg90CzwQLfAANLpD/PqdYzHrcfgU3tjf1PW+3hXkkp+tQ5LAFVDYctLBG/saKEiz8fmLy7l8Sh7/2VJFUFapbE3m8imna8ScfSz7zdao96UPrI4r3PfH2VK2U+ABWPLo67z3wOVd73/y6n5e3F0HwIpfrUu4DUPFrhp3zM+DwPM7atEBasdnu2tcXce/8NQuclPM+AIhXAGVyFuvzRe+21q94f+bPCF2P7MLvSQhKyo6nYTFqMPlV9CAtQebeG1vA23eIP6Qgt1sIM1mpCDViieoMK0olfllmVHti/wN+ur/u5/cwvtHWwCY+5P32PuDy2Oe1/M3Hcy1Lv7p2zg6NChX/mZz3DGS6LX6QihUEud4swfPMAk8wJAIPND7N91V7WTtwUYArptVxI9eO9Rv+YpHV7L+SBM/eGU/mqZxotnNDz8yteucJzcc4/kdYZfolb/eELP8qTDvgdVsGkQdz26rxuENkZVs5hPzRw24vKyo/HtLFb6gwqhMF6tmFQ24jk3Hm/nR6gOomsZHH/sw6thQ9M3ZzLFGN19/ZjeKqrKloo29te0MZpXT6ld552Azaw82I0lg0Em8daiJZz+7gGSLccjbPdIYiHnbfEmSrpUk6V4ASZKSJUlKGuR1twHLOl4vBLYMsp5hp6qn+rUHfjn6BA1wBzRqnf6uzxS1hySQIIqiEFDCyx1/qJ+GnAeEVBU5oi8VTSOkqvhDKiFFI9TVV2q8KgQCmtuj76UGV7doLisjb+z0NXsoqoaW4PSiqhqKGhaONEBWtK66NcAfkkEDTdWQFa3rnlJUDUXVBn1f1bd3z4UhZXBzYaK0uIPDWr9gcGiJDtIRRuRzNyAn/gx2+IJd39kViFb31UfsEqnDMN00DLJc5/0dGORaQ9E0QvKpPYOb3YGuOSkwAufiM4nbH0LtGFNDsR7UOv7RNAjJ6rDPzSOFgQg9fwRuAr7c8b4ceH6Q130OmClJ0vvAG5qmtQ6ynmGnU7MQT8MwPjeZJeOyuXFOIakWPcvGZzNjVDqfWTqWyQXJ2Ex6vn/1pEFdW6/X8+0rJjK/LJPvDbKOkc6iMfHN/orTzBSlmNBLkGzW8/tbZ/GNy8aRZTdRlmXj5jlFfHJhKVdMySPVauSqafnMHpXOsok5p/EbnH3ctTBaA3cq2rOeZQdS1+ksa4uY6fb/OPrcX9w0k7IsG3aTnt98bEbCbRgqkkzxj5VmmJlSkML80amk24yUZVowSmDQwe3zS7hrURmXTs3novFZFKQYMUogAVZ9eHLXE97OT7Ma+MTcEj52QQmzStJZPjGXe5ePZU5JOiXpFi6ekMPD105m1ewiLp2cx91LyrjjwtF8fN4obl9YypJx2SwZF23AFdnnffX/S19cTHaSEZtRxxO3z4l7Xl91JHqt3Q9d0fX6U/Pjx8fpK+pOouPwfNY6D5SCNAvZ9j4G+gih5286e1Q688syWTo+mzE5dpaNz8asl/otf9W0Qm6aU8KSsdl864oJUed848pJlGfZMBt0PHj1hJjlh/I7JMpHZhYwa1Q6V08vGFR5s0HPNTPCdVw+JW9QdaycVsgVU/IoTrfxyxumRx073++36SXpfOrC0UwvSeen108jzTz4uvJTzCwdk8n88gwuHJPF1y4bT0ZfD6JzCClRDYwkSYc0TRsvSdIBTdMmdnx2RNO0scPaQmDOnDna1q1b+z/xPGTOnDmIvomN6Jv4iL6Jj+ib+Ii+ic+Z6Ju4ZtcjbIEoxk18RN/ER/RNfETfxEeSpG2apsXUsA1kp6e1I7S01lHpfOjleysQCAQCgUAgEAgEI4qBhOj4PGGztBJJkrYAhcBtw9IqgUAgEAgEAoFAIBgiBiL0HCScX2dCR7lDQO5wNEogEAgEAoFAIBAIhoqBmLetB3I1TdunadouwglF1w9LqwQCgUAgEAgEAoFgiBiI0PMz4F1JkmZLkvRx4D/ALcPTLIFAIBAIBAKBQCAYGhI2b9M07V+SJO0Fnga8wEJN02qGrWUCgUAgEAgEAoFAMAT0KfRIknSE3rnx7EAqsFaSJDRNGzdcjRMIBAKBQCAQCASCU6W/nZ7lp6UVAoFAIBAIBAKBQDBM9Cn0aJp2MvK9JEklhENVR6YkjjpHIBAIBAKBQCAQCEYSCfv0SJL0c+BGIAnYD0wD9hAOYy0QCAQCgUAgEAgEI5KBRG9bBYwBXgeuJxyyum4Y2iQQCAQCgUAgEAgEQ8ZAhB4/oBLe3VkM1AKLhqNRAoFAIBAIBAKBQDBUDETo+T7h3Z2/A/8HVAMvDH2TBAKBQCAQCAQCgWDoSNinB9gMfIZwIIMtQBrh0NUCgUAgEAgEAoFAMGIZiNDzMvAG8AogD09zBAKBQCAQnC2UPrA67rGKR1eexpYIBAJB3wxE6DFomvaVYWuJQCAQCAQCgUAgEAwDAxF6fixJ0u+B54FA54eapr035K0SCAQCgUAgEAgEgiFiIELPAuAjQDmgdHymAULoEQgEAoFAIBAIBCOWgQg91wLlmqZ5h6sxAoFAIBAIBAKBQDDUDCRk9f8IJycVCAQCgUAgEAgEgrOGgez0XArcJUlSDRAEJEDTNG3csLRMIBAIBIKzjHjRzEQkM4FAIDizDEToWTZsrRAIBAKBQCAQCASCYSJhoUfTtJPD2RCBQCAQCAQCgUAgGA4G4tMjEAgEAoFAIBAIBGcdQugRCAQCgUAgEAgE5zRC6BEIBAKBQCAQCATnNELoEQgEAoFAIBAIBOc0QugRCAQCgUAgEAgE5zQDCVl9xthT44yb+yBRcg3QIHe/v2FOEfcvH0fQ2cjS3+8F4JGPTOab/9vXb11j0/UcaVOiPitOMWA0GGlx+3AGe5cpzbBw8YRc7BYjX7l0POsONfDrt49y+eRcUm0mWj1BPH6ZhWMyKc5IojDNyoH6dixGPeXZ9qi6Kpo9uPwykwpS2DsEfTPcmIFAP+cUJBupdYWwG2FsXgrVrV6aPHKv83LtBprdMgqQZzcwPi+VZZNy2XSihQ+OtlCencSvPjaLogzbkIybc40kA2z+zqVRfRMrf0hkv/U83texkVD2K0vL+OIVE6OO/f6dg/x0zTHyUkx8+K0VvcpuOt5CdZuXlVPzT3ncWHTgV8Ovx+daOdHkI6iGE5stHJ3GmPxU3j9Qz/G2AClmHV+9fAIfHG3hY3OLeW5rFW8dbKIgxcxHZxUzpzSdD463oJMgw2ZGkjRcfhm9JJFkMTA+NxmTQU9lq5fcZDO17X7yUyw0uQOkWo3YTHr+ubECt19h6dgMggo4/QpGvYTdbGBifgqlWUnkplgAkBWV/XXtpFlNlGTaen23Iw0uSh9YjUUPB38U/RtM+d5q3EFYOiadJz+9MOpYf79dLGZ8ZzUOOXaZFneAylYvY3OTsZvPisfYec1g76krxqfx2iFHv+cVppoZk2vnyU/N7zNP0pyHXqfZq8Q8DpBi1nPppBx+dtMs1h9porbVzdNbqmn3BznR7O81f/S81j0Xl2M2SJxs8fGZJWWMy0vpt+39zcWJMJj7qyc/eHEPz22v4Zb5xXzjismDquM/myp5fX899y0fy/Ti9EHVsfZgA9sqHdyxsHRI+uZc4uGX97H+WAuPrprC0X7uqVQjrP/2pSRbjKexhSOfM/K0kCRpHPA7IA14UtO03w73NRt6rJ+f2VrNwbp29tS0d32WiMAD9BJ4AKraZaD3Ir2TilY/T3xwkmSzHqNe4g/rjuMPKeyoclCeYabKGcJm0rPmQAMrpxYwtTCFXdVOAFbNKmRUZhIAdU4fL+yoAcDlD6El1OIzS38CD0CtKwSAOwQ7qtrjntfg7u7jerdM/dEWNhxrQe7oiK2VTj795BZe/dKSU2nyOYtHhmW/eDfqs9IHVkc9UAayOBkpZUdHlP35uuO9hJ6frDkGQF17kAee2cGjN8zsOna4wcUv3zqMpkG1w5dwG+LRKfAAHGrork8DNpxwsPmkg1DHOe0BlR+8uB+9XmL90WbcgfDccqzFx2/XHiXVZsQXlAnKGmaDDp0OArKGDkiyGMhJtpCRZKTdJ+Pyh0ixhl8nmfWEFBV/SKGixYumwYajzSCFhS9Vk7CadIzLTWbRmCw+vbgMi1HPB8da2HayDUmCj88rISfZEv3d5HDD/Qr8ae0R7rp4LAC/emMf7g5lz7qjbVFlBitAOiKm0xnfWc3Oh8O/t6pqPLOtGl9Q4WC9i5vnlgyqfsHIJxGBB6DGGaDGGehzrCUyDtsDCs/uqOOicTX8Y3Mlu6ra8MvdT9nI+SNWfb9bewydBDaTnu2VDtZ+9aKE2h/ZxlNd3A+2jic+rATg9+sqBiX01Du9PPjKPlRVY0+1k63f7a1c6o/jTW4efGkfiqqxvzZ6HTAUfXM2s+5QA3/9oAJVg1v+vBkf4QV0PJwhWPXbDbw5wDF4rnOmzNv0wDXAfOAuSZKkM9EIWT0zIoM/pKBpHdfW6HqtaaCoGhoaQbV75RTZTiXi9Zlq/0ijZy/IqnpWCINnCllR+z/pLGMgv3dQiVZayKpK5+0Ykod/5Gg9LtH5Vunxu2hoqKrWdYKmaeGymhb+SNNQtfA5GuHXGkT8Hz1fqFr42lpHXWjhc9WO/6H7fE0DtZ9hUuvwdL1uaE9EtTF4QhECkEZ3O8UcKBgOfB0DLtbwcvvjKzcjUfq7gc4xFIWuuUrpOcklSEhRu/pcPs/6rz98IaXrWaEm2L8BJbGxej5xRnZ6NE07ANAh7Mia1vsXlCTpbuBuAH1K9ilfUw9ELnUunZTLVy8dx/4Tbdz3Yti8bRKwP4G6sqzQ3EMhnGGR0OkNtHtDBGOMxwyrgaUTckky6bl/+Vgm5Kbw27VHuHBMFsUZSdQ6vARklcVjsijLSWZ0VhLZdgsWoy7KvK0o3caVU/Nx+UNML04bYC+MXOwmcAfDA7Io3USTJ4gnhplg53kAdiOMzkph2cQcPjzRzJ7qdgrSLPz+ltnodWdEjh7x6IA3vryUgse6P+upPat4dGXC2vmRWPaG6bm9zv/Y7AL+va2WZLOeX3xsTtSxSfmp3L2kjOo2H6tmFfLthFoQH4luQSY32UCjS+56Py7XzqS8FN47XE+rT8Ugwb2XjOXDihZunF3Mc1sr+fBEGxk2A9dML2ReeSYfHG1GA7LsZjSg3R/CKEkkmQ1MKkzFatRzotlDjt1EvStIboqZFneQFKsBq17i75sqcQdkLhyTiSxrtPtljDqJZKuJacWpjM1JxmYKPwouHJOF3WIgzWokL9XS67sZOu4rCXjwozO6Pn/khlk8u301IQ0m5ESbxQ3kd43HvojfW6+TuG5WESeaPUzMTz6legUjm5kFNnbUehM6d1pBCi/du7hP87YJ316NP751G2a9xLzRGdwwdxR2m4mLx2bx3+3VtHkCtPkU7CYdv/3EBQB8czQ8ciK6/Cfmh3cd651+PndReULt7tnGU2WwdVw2OZt3DzZz9bS8QZUvzLDx5eVjWbO/gS8sGzOoOsbnpfCVFePYUeXgrsWj+U/EZHw+7/IAXD6lgGunN7DtZBsPXj2JlX/u+3wD8OznFvZ90nmIFEPeOH0Xl6SfAY2apv20r/OysrK00tJSAPbWOLsWEGF79vM7FkNFRQWdfXMuEPn7js2xYzHqB13X6eybJleAFk8QnQSjMpIwG/sfl53fVS9JTCro3/Z7KNl76CiG1FyMeqmXz1ggpFLr9KFpkJ1sJtlyenQjLn+IytawKVaazUhRem+fktPBjv1HMKTmADA5Pxmd7vyeYyI5nffUUM4Fp4ORMhfvqQmbRUvAlMLUM9uYDkZK34xEIuebs2Gcn052HTiCLiUHibDCyGQ4f/tGUTWON3tQVI1ki4GQo6Hrntpf1961+51tN8dUWJ1PbNu2TdM0LeaDe9hXM5IkvRvxVtE0bZkkSTrgCWCXpmm/6K+O0tJStm7dGn4docV54COT+Pj80UPb4LOMOXPmdPXNuUDk73vX8nK+tHzCoOs6nX1z99+3sq82vNi4b/k4bphT3Of5Te0B5j7yFpoGOgm2PnJ6tVgpxeOZek94u+eNLy3GbjV1HXtpVw1PbQzbdy8Zl809Fw9OazdQvve/vfx7S/i6JRk23vrKRafluj0x548l//ZfAfD4Z+YxZ3TWGWnHSOR03lORc8E9l47lc5eMOy3XHSwjYS6ubPay5Gdru95vHSHa8ZHQNyOVyPnmbBjnpxNL/ljyOvrm69dO4tYF5+96b2dlG/c8vR2A3BQLlX/9Elu3bsXtDzHt+2voNAackp/MK+e5T7MkSdvjHRt2FaamaRdF/C3r+PhWYEsiAk9PpnSYM9iMuvNe4DkXmVkU1kzajLpTEnhON3cuHk1+qpWphWlcNa2g3/OzU8yMzbZj1EvMLc04DS2MJiPJRLbdwsppBVECD8Al43OZXBCO6nXNjP6/y1Bx77Iy8tMsJFuMfGnZ2NN23Z6YO3aPU8x6IfCcQSLnArEQTIySLBvptnC0ppIM6xlujSARbB07O2Kc9ybVasSkl8hLtpzXAg/AjJJ0LhyTRXayhTsWlnZ9brcYuWR8JhJg1El896pJZ6yNZwNnxLxNkqQ/AHMAd8dHn9c0La47zZw5czShJYqN0KDFR/RNfETfxEf0TXxE38RH9E18OvumL3+b8xUxbuIj+iY+om/iI0nSNk3T5sQ6dqYCGXz2TFxXIBAIBAKBQCAQnH+c91nd7nlqO/vr2rl8Sh7fuPzsMac6F9hZ2cY3X9iDBPzsxulMyh8ZTrd9saOyjY3HWxmbY2f5pN5RwvrivcNN7K11MqsknfllmQmV+eBYMzurHEwvSuPCMafH1Kqx3c/Dqw/gDynct2LskP4ue6qdrD/azOisJC6bnMsZilbfJ96gwu/fPca4XDvLJib2G1e1enn8veM0uQOsmlXIpZMGFwFJ0M1j7x7hma01lGUl8fgnZqHXn79OzGcbvqDCp5/cQp3Tz52Lzm+zpNPBj189wJr99UwrTOU3N886080ZMppcAS7+2buMyrTx+C2zMZnEHDAUbK1oZXNFKxlJJlpcASpavIzJsXP19AIK0s5ts9izOixRTZuPD482D7r81ooWtp1sJSgrvHOgYQhbdnppcwdZd6iRYLCPWJxnAKc3yP5aJ2qcePv/21lDuy+E0xfipR21w9KGxnY/AXno+mVHpQN/SGFPjRN/KPF6NU1j+8k2Wt1BNp9o7ffcho52bz/ZRiCksu1kW59lArJCY7sfTdNodPn7bZumwbpDjTh9veOCf3i8hYZ2P05fiHcONPY6rqrh9gXl2L+ryx+iNVa8cWBHVRv+kMKBuna8I2y8duINylS2ethwtJn69tjJSt1+mb01TuSOPthb4+REs5s2T5D1R5qj8uOcL7j8oT7v94Hy2p46nN4g+2ocHGp0919AAIDTF34e+M7g/bX5RAvHmty4/CFe2T08c/u5gqZp/HNjBUcb4yfl7o93DjQSksPPiVhz+tmK0xei1e1nf42D/fWD75/zgd1VDg7WORM6d3tleF2x7lATdU4/la1e6p1+9tW29/l89wUVGl3+oW76aeWs3empavXwySe24A8pXDY5j+9dPbAMwi9sr+HfWyoJyiomg455CWreRxqaBrc9sYlWT5BJBSn86bYLznSTgPDC8evP7sbhC7FwTCZfWtbbQfOySXm8f6QZCYlLJw+9ZrzdH+KpTZWk24x8YkHpkOTumVyQwofHWxgzwNCikiShobGnxkl5jr1r3MVizf4G9te2k2k3MTE/hT01TqYUxg9pLSsq/9pUSZs3hEEnIasadrOBTywYFbeNJ1s8fOuFPWTZzTz/uQVRWvQ5pems3l2HX1Zi7i6t2V/PgToXWXYTt8wbhS6iX1vcAf61uZKQonH5lDwm5ke3e3JBCuuPtFCaZcM2QrV23qDC2oNN6CRIthi4ZkZhVFhvVVX55gu7aWwPMK0olW+vnMSE/BTyU600uwPMGZVx3uWJCsoq33huNy3uIHNKM/jaZeNPuc5ASMPhC2E26MhOMvVfQADA7X/ZQpPbz5icZJ781Nwz0oZRmTa8QQW/rJI0Qu/zkcLRRjcPvbwfk0HHW/cvIS914KH6541OZ92RZibkJZNqPXfulZCi4vQr6CQFu/msXa4OO//aXMnv1h5FAn5wzWQu6cdCYXJBKlsqWpk7Op02T4jsZDNpNiPjc5NZs7+BA3Xh9cetEc93f0jhHxsr8AQU5pdlsqD87Fwzn7Wj6Giju0ubfbQpWgsoyzIPrT4AwPdWTsRg6P01j3WUGZubzGeXlHLxxPxhbvHwoGgabd4QAFWtsbXSPXli/XF2VDn52mVjKc6w919gELR4gjh84XadbI6dXG7BmCzW3Ld42MxWZCWsbW/zhvCHFJISmDR9QYV9tU7yUi0x88TMK8tk7uiMQZllFaRZmddR1hdS4go9De1hTUqLO8iNc4q5ZEJO3Ov5ggq/eusQB+tczChJ52SLh8J0G+6AjDsgxxV6ArKKwxvCH1LxBRXs1u7zCtNsPH7bHFRVjZmjpt7Z0T5PkKCiYtF1l23xBAl19Ht9u7+X0DN7VAazStIH1X/eoMz+2nYK0qzDugWvahoWo4QvqHC0wcPz26v51IWjybSbO9qh0uQKAGGzNoDRWUn89PppAKdssnei2UObN8iUgtSzJg9Zuz9IS0fW4MpWT6/jP19ziPp2P9++YjxpSYnlkLCZDeSlmJEkCadPJvv0prIaML6Qwj1PbWPZhBxWze47ZP2wtSGo0OwJj81aR2LPg+HAG1KYmJ+CqqrkpZ7b5jKnSlBRCSoasqJwoskzKKHnR6umoSjKOWcCqnYG2tLgZIuXMbkiGXEkFc0efvXWYapavWiahgbsrWnvV+i5cEwWC8szw8pYTYv6f+2hsHVHa4/ne7s/hCcQXnN3rlHORs5aoefiCbksGdtAZau3Vx6RH756gBe213S9f+jaqb3K3zS3mHZ/iIwkE4vHDsw3YyRh0ElcP6uQjcdbuemC/h+0m4618Mu3jqBpGiea3bz8xcXD0q7idBtXT8tnf117nzlrhnOStpsNjMq0UZqVlJDAA/DmgQaONbrR6yQ+tWh0TO3SYBe1i8dms/F4C8UZNlKtxrjnXTw+hy0VrZRl97+b9MhrB3j7QAMBWaUow8qt80dxosVDXoqVrI5Feiw0wlo0k0GHKc5vEC8p58UTctha0RZzt6s82860olQ8QYU5o9Jjlh9s/72xr56KZi9GvcSdi8qwDpMG2WzQkWQyoNfpON7sotHtR6/Tcf+K8G6l3WLghtlFbK1oY+W0bmXJUPgnNbr8vLizBk0DpzfExRNyTrnO00GW3cJHZhayp9rBR2YWRh3754cV/G1DOHV9qzvAXz6Z2O7DZ5eW8+QHJ5halHZWLHYqW7y8d7iJDUdbWFCeRf4ZsI23mvTcPLeY9w83c+3M0xduvieT8lNZPjGHihYvH59fwutnrCUjH13HvCHpJHJSBj9mzjWBB6L7pliEYO/FF57ezskWDxowJieJvBQbty0YlVDZzudVz/8vHp/D1pO91x85yRbmlWXQ0O5n4Zizc5cHzmKhB+CR66bF/NwXULr8BbwBOeY5xek2Hrp2yrC17XRy34qBmZL4QgqKquGO0zdDxa0LSoe1/v4wGXSsmlU0oDJOb5ADde2k2YbeRKAgzZpQe4ozbBRnJKbt8wZkHN4QkgQzi9OZURL+G05GZSYxKjMp5jG9TkrY+X+wDHeUfbNBzyUTc9la0UabNxDznOtmF3Pp5DzWHmrij+uOkWk3M78sI+bu4PnCzXNLuHluSa/PAyEVb0hB08A1gDnn8il5XD7l7AkIoWoanqCCUX9m/bnuuXgs91x85vJcdXLXkvIz3YSzAolwgmr9CAzqcqaRJEn0TR84fSE8QQWDTuJrl03gwjHZp1xnSaaNkszYz7GF5Wd/3rqzw3aCsB39Fx9YjaqqNDY2MvuB1axbt67r+E9+0h3/3yIpmA06JA1SLAYOHjwYs06n00msPEV1dXUJt6kTWY79MD9deZAqKytxOBw0Nzfj9/vZsGEDTU1N/O1vq5FlGa/XS2trK/9+diMWPaBButVIKBTC6/V2tbO6uhoARVG6Pmtpaem6TmtrK6FQqOscoCtWvM/nC2+xdpRTVbXrHOjuo2AwiCzLVFVVdZ3beV7P8zVNIxAILzw7rwuJ96uqqtTW1hIIBKiurmbHjh3IssyxY8dwOByEQiFcLheqqtLU1IQiy1j1KjolSEhWUBQFv9+Px+PB7/ejqiqyLKMoCg6HA6/X23UdWZa72tzW1obX66WxsRFVVQkGgzidTrZv344sy131ybJMIBAgFAqhqiqqqtLQ0IDH4+mqKxgM0tbWhs/no729HVVVu8qYNR9WvUayScIkKV1tCAaD+Hw+3G53r36F8IM2qGjoJImgEtvh+ZlnYufUADhy5Eif/V5VVRX32Jtvvtln2SeeiH3dyybnMS3XxHWzi+Lu8gSD8Z14E3WwT7UZ+X/vHKUs04zc7GZarpXbF4zC6XR21RMKhVi3r5qX39/Bmr21/OGZXfziX2sJBoNdY0HTtK5+7/xdItsROWd0juecZAvXTC9g6fjsLn+qzmM9/49FvHMG+n6wxMrDYjLqsBr0qBqk2Xrr2QZ77Xvi5Hzpq854z4KhQC9JqBpYDTrOdByLePlwTjd/+MPIaMdIRq/rGDdG3Sndh2+99dYQtmpkoJfo6ptOc3VBN1ZTeK4JKhp/+fNmXnllNa2trciyPGQBZc41zkhy0oFSMn6qpvvoowmfLxE23+nJ658sZ8KECVQ2O1nys/Vd5z77uQXMHpXBsv97h2MtYTvoJCPs+2H8hGk/ee0gO6raWDIum20VrWypaKMkw8Yr94bNxRRV4/nt1dQ6/CybmMOUwuEJx5w9eiJJN/3slOvRA5FL3ySTjlBIJdjRkQYgUqyTgLxUM3XO3prwj8zMZ9m4bL794n5CisqqWYWsO9REmyeIX1bpOXetnJTF7joPng4flMI0C8ebvQRCMu6gigRkJRlpDyhYjTo+Mb+UJIuBVTMLyUmJ7x9QNnEa6rWPDK5DBki8MTcQev4GQ0lxmpVrZhZy/4px6HUS5vyx5N/+KwCO/ejyKNOIngumnokDI4+/eec4xo7t1ipf+cu17G8IC4IScKKPsn9aZmLFihUJX3f0A6u7+vid+xdSltO9o7W7qpUb/7gJWdX46MxC/u+G6VFlX9hew3+3VlGSaeOH107p01cmsm9iYdGBv5/nSWf1qVYjekmi2RNEIrzDnJ1ixuULUev0U5Jh42uXT2BHZRtTClLRgH0dYc2XjMumqtXLiztrONbkpiAtbLLo9IWYkJcStQviDyn8d2sVTl8Ii1GPJyCzaEwWY3OSeWZbFSFFY9WsQnJTLKzZV8/+uvaoa7y0q5Ykk54b5hT3aQoaLyFeX79dX8dqHD5e3FmDxaDnhjlFJFvim33Gu15kff6QwjPbqnF6g1wxNT8qAEVkGRNweIgTYkaOGxuw/wwl3IzXN6eTcQ+sJlL9kPXWgyI5aRx6zjeD6YuR8JsPB0PRN+cqs2bPpnXFQ3GPJxl1fO6SMXx6UdmAgi6dC4y45KQDxROQGYhFd7zF51deq+H3NhtLHtsTde6tf/yQAz9e2SXwAHhCsLfKQWqSifQkU5RvhyyrvHOwAZ0ksflEK3trwjtGJ1s8NDr95KRacHiDVLeF69tf2z5goccXVGhyBShMt6KoGvVOP7mpZsyG6MHb7peJbWg0MHoutj3B6FVdz30sDeKGJf7fjjoO1Dho94dLPbWpCj2gEvu3Wb2/GWNHG+wWlT01QYx6CXdHGzSgyRNCL4FbVdle2cq0onRe3FXDxy4owajXcazBxQfHmlkxOY/SrPBCp90XYnjCNPRmKFQHwxlgtsrh43drj9LW7uHHN0TncSj/9uuDfph8/bUaXogQejoFHui/T+56O0jFin5OiiCyvkdeO8Sfbp/f9f4fG6vwd4TYfPdQ7zDb7xxo4HBDO81uP1Vt3qjF8EDpT+AB6Iz26fSGkCMaXtnqJaio1HYEgzhY2877hxppdAeoavFiNelw+mScniBmg44aRziU6IlmD5oGRxvclOXY2XS8heUTc/CGFNp9IQIhhRZ3kKo2DzWtfmaXpvPBsRb8IYWKFg+yovLB0RaumJrL/rp2NA32VDswG3RUt3kJyipBWaXG4WNchP9MQFY41ujBatQzOnsoZppoDje4CIRUAiGVqlYfkwq6hR63J8j/vXWIFZPyWDS222yjr12Meqefdw800OgKUJZtj/s795y51q5dyx1vhMfuQO6FJY+8SaUzyHufj/YbjR26ZfgZKTs8507Q5LOPK/9vNa9+TQgH5zouX6jP456Qys/eOMzP3jiMRHixbzNCqt3MojHZXDE1D5vZSGaSGb0erEYDWXYzAVmhwRkgL9WCUS9R3eYj1WYkJKvIqkZuH4rmU6Gh3Y9BJ3UFDBouzpjQI0lSDvAXIAtYp2naA/HOTbYMTTNXf3lZzIeCT4WJ31nNjIJkdta6uj6//vEPuW1BCZlJFm5fWNolLf/twwpqHT6Cisb0olRSyrN470gT43Ls5KSGB0S6zUR5jp2aNh/Ti9MG1E5F1fjX5kqcvhBjc+14AjK1Dj95qZaYNvNnCrNeIiDHXtoeaoyOHNTfgr7z9vUGVdKTjDFzTCgaGHUwrySDt480oddJ7KtxMXtUGr966zDugMKf1lfw3lcuwmIx4ArIp03oOVt4elsdBxo3DFl9o+29I3WdDkyh6GXlmj3VXa+b3L2XXO8casATVGnxhGjz+OEUhJ6B0PP2UKFL4IGwMuHdg3XUu2V0koRBLxGUVXSSxMYTrZgMOixGiXpnAL0kMS4vmf217aRZjazZ38CJZg9BWWXWqDSaXAHWHWpC0cDpDzGtMJWTzW42n2zDE5Bp8QTwhmRmlqSzr9bJ4XoX7x1pxqCXuLA8i/QkEyU9fMme3lTJm/sbsJn0fGoYkkxOyk/hWKMbq0lPaVb0tZf96j2a3AGe3lzFm/ctobTjN3vyCju3vxY7b8+Gw428c6gJDfjx6v1cObU70ISOcP8DpPV4rnYKPABjH1jNkQQEn8t+/g6VzvBYi1SknUkqHl05YgQfwZnhla9ccaabIDgNpFiNtPR/GtARuAhwhsDZFuDpLdU8u72aTLuZURk2ksxGphalct2sItYdbqLJFaAo3Up+qpUtFa0EZAW9pMOgl7hyaj7j84Y2sMzB+nZe21OPTpK4fk4RhcMYBOZM7vRYgU9qmtYiSdI7kiTlapoWM0Nokl7lVJdW//vshX0e98nwv3uX8Nf1J/jJ6wcJKSqyouENKliNCoGQ2iX0nGh2k9IRfasky8bnL+rtNKrTSVwzfXDRc0KKSrs/LAa0eYK4O8IExttZORNMyk9h8dhMLEYDKyblctX/W99vGaNOItSHsbteBxlJRkoz7WQnm3h1T32Xdl8nQZJJz6jMJD62YBTbOpKDNrv9HSGpVTRNwx9SaA/IWIZIUD4XGcpwk88fhl8MWW2Js7spWsvl7OfWiLQHP9rgYU7pyHHI9IYULEY9gZCCooDFqMcfUpBVFTWkMbUoA9AxLjeZUZk2cpLDipV6Z3cCOZdfpiTDRrLFSEhRsVsMZCWbOdboJsNmAi2c96bNE+SWeaNYOi6br/x3JxDum4/OLIwZbayh3Y+iaviCCm2evjWLgyE3xcKnF5fFPNbQERZcVTROtnq6hJ4/H47/Y++ocXTNGS5/9P708QR3cBL9lieaz1xI6HgIgef85LJJuTh8obCPpqxiMZ017tqCQXNqwR1kBYKyRrtfQafToWnh9abDG55f27xBzB1r3nZfCJvJgEFvGJZ1aOezRdU0HN7guSn0aJp2MuKtSh/PmqOtQU4li47NqGNGaRoAuXYDDe7eQQf++9kZAKyYlMuemjY+PNbGzJJUFpRlU5RuJdXWbXbxpWVjafPKWI067lgY+4F9KliMei6bnMfxJg+zRqURCKnsrXUyKf/MJKqYOyqNzScdXe8NwBVTcpmYn4pBLzEhLxmjBKEIeSbDZqTV2/2T2o0SBRlJ2I06dte2o9dJBGWta4Fi1ksUptlIsugpz0ni25dNYF+Nk7p2PwVpZvJSbIQUjU9dWEpOioW7Fo/mnYONLB2XTWG6ldo2H9tOtnLxhJyu3bY0qxB8emLSwU+vn8qy33R/1p9CoC8OPzx4reJ/754X9b4QqIl9ai/e+MqyqPfP3jaB6/8edlIvTOn9u39x2Vj+sO44xRlWPjYvsZCep4pFD1dOK+D5Hd0Z6ZPNerLtRmocfgIKWAw6vnvVNF7aVYtOB4XpNipbPGTazegkmFyYQrrNzITcFNJsRuaOzqDG4aPO6Wfe6AyON3todgVYWJ6FomrUOX20+ULcfEExTp/M3NEZ7K5y0ujyM60oldmlGV1tuXPxaJ7bVsOUgtS44ZVvnFOM1agnzWZiUYxEtZ30tcPw0CT43v5BdmIHX/3PVrZ8Lyy0/OOuFXGv9bMbZrLpeBtOX4gfXDNpUNdK1Lzt8CPd3znTpiMy/M2Z8j348Y0lfOu/lWfk2pGIHafBkT3I4I93Lh7N63vrmV+WicV0bj73UhJz9ztvOJWAdgYJ5pVlUJRhC6/f9DqK0m1MKkjBZjZwsL6dqYWppFiMGHQSF47JwhdSCMoqM0vShuw7dDKzJA13QMZk0DEhb3jXuad8d0iSZNQ0LdTjM4OmaQnFJpUk6YvAYU3TWnt8fjdwN4A+JToM36k8UDZ957I+jxdn2PjlTbNiHpv64Ou4AwrzR2fwr88siDr2r80n+c/mamaUpPL9a049FPbE/JSoxI6lWbHt6W2msOO/xajnlS8u5ov/2s57h5uAsNTc6VuQZTcSCKno9Tpe/sLCmElJL/7pWk60dpt5RPbzxO++hi8UDipwQVkmV0zJ78qd8e0XdiPpJDItBv5+51w++dcteIMKj3x0CjcnuMAMBhWueWwDze4gVoMee5KJd79+ScxzZUXF5ZeZmJ/CtKI00pNMPHp97/DlxRlJbI34Dg+9vIe/bggvCDrDhOp0ErNL0shJsdLkCpCdbOLmeSVsP+kgKKtUtno40eRlblkG37pyIlWtHq757QbavCEkYHSWjaunFyIrCn987wSapqHTSWTbTeg6zJHSrEZ2VjnJTjYzMT+FVKuR9UeaaHEHafUGKc+287G5xdx0Qdh08UBdO7975wieoMINc4pZd6iRN/Y1oNNJPLpqKpdODjuwbzzezP921JKfaiE3xYLJoOPSyXnYzQb+uv4Eaw82cLDehQbcNKeYr10+IWZ/pvUYWmWZVo53+LfFCpI2vyyDHZUOclPCC/NIbpxVyH87cmQVp/deSBv1EiFFQ4JeOZA29HNf/+bmmRxrdGMy6LryN3RSUlzIfcvDYZEviFjYd/KFS8byhUtOPYyvUS/xsQuK0esk/rM57EcUuXdp0kv8+qaZXBGRv+cXN83E6Qvx5v4GLEYdl07K6xVIIfL8/shJsTCz43VP++cfxAjDf9H42Ll+JuWnMumqvn0Ny7LtfGn5uITaFW9evu22ldyWUA3xybbHDiFviPHgf/8bseeNvhjsMyWy3JyXUqPmmzPB1BGyMPT5fFiNOvwhlZVT8th47gUWGxbijfP+mDs6k7mjz97cKYlgt4qw1e8faaK6zdelgKp4dCUrfv4uR5p620KZ9RIGvQ6rSc+oDBtXzyjg1nmj+P27x6ho8fDxeaOYHSOX3uisJA43uHj8veOMybGzamZRlNJ/OLAY9ayYdHryZQ6FSmCXJEn3a5r2OoAkSSuAXwP9qtkkSXoEMAJf6HlM07THgccB5syZozV3lYGm9gAfHmtk4ahkMtNTE04K2OIOkGY1DCiJV0BWMOl1PP7eUVwdZmYbT7T2Ou8fH57sWNj4uWtxOYUxFnwQzgOTOoQ5YIrTbdw0p4Ql47KwmvT8/uMz+O5L+7Gbjdy6oIi7/76DNIueH1xRzj+3N7N0QnYvgccfUqhx+MhJtVDj8BJU4V93XRB1zh9uncVja48xIc/OR2YVk51ixu0LYreaeG5bNUFFo8UT4p6nttPSsf35q7eOxBV6Ovu187d7bV89Tm8QWdU43hzbXr+TE80eDtaHfa92VLVxyYTEbpZvXTkZhyfEoUY3n11azpYTrXgCCtfNLGTNgQYqWz1ImNh4rJWb5xXT1B7ky//diS8o88quWj69qJTijCS+dtl4/rjuGCkWPR+fW8KU4nQeX3eMjCQj7b4QS8ZmkpNqRZbD4Y9tJj3Xzymi2R3k8sn5VLV5uWRCDieaPbR0CFqRSS6f2VbNwXoXTo8PWVG5Yloe1Q4fyWZDVx6YkKLy4o7ajr5oZ3SWnZIMG3uqnVxQms61MwqYVZzGt1/ci6pqbD3Zhssfihkdy26JHo+miEgvphi31p5qB7KsUtPqo6LFzZicbuH8ziXl7KhspT0o850rei+Wn/jkbL734j5WTsljUmFaQr9bJ5dNzmV7kpnyHFuvaDQ5yRaun11Eu08eUntjqx6SLAaMkoTBoKc4w0pmkhlF1chPNdPiCWIz6rEYdSRbTFw3szCmALOzykFVh0KhLMvNpIIU/KGO0PrnSA4KTdMIyGrMSEGbj7ZysqWNG+YlnrtlTpGNrdXhPnv1/u6IFy9t7zYSiOVSKMsqfjls4ne6+f07B7lpRg4ZGb0F79NBan45cOZ3er790kF8obDG7bV99Qxv5rBzh9xU4YUaj+L0M2PpMlJo8wTZWtEGwAfHwitilzfIjbML+O1bR3D22GYIKBoBRcETVPAFFYx76xmXY2f90XDZZ7ZWRQk9QVlFr5OobPWy4WgzRxvdOLwhSrOSuLhDcTaQZ5ashBWCRv3IMrUciqfCNcBDHTszCpAB3N5fIUmSlgBGTdO+mshFUiwG2v0yJp3E/Efe6gp7/PmLR/Pl5RP67dgfv3qA1btryU628M9PXYDd2r/gse1kG+8dbiI3xcJFE7J59LXDaIR9S3oyNjeZrRWtZNstZCfFrvuhl/exr7adC0oz+OplA0soGg+TQce9y7s12EajkUev6w7Xe7zJjazCFX/YznOfm8fsUdEmKq/uqeNQvYsxOXYy7SZmjsrg0km5LCiP1g4vHZ/L0vFh4eLtA/Xc95+dSBI8+tEpGHQ6QEECVk7J44/vV6ABkwtiT1KR/XrjnCIMeh3Ti1NJMhvwyyqzR/W9YMhJDu9sBWQl4WSQje1+ntlWTXluCg9cOYmcFAsLy7P479ZqfvH2EaodXqwGPalFJgrTrOSlhP/G5tjZ3RHl6qlNVcwsSePyKfk4fCFCskZWipW3DzTiCYS/v6zC9konf7tzHJuPO3D6Qlw4Jos5EbsPnRGl3j/SxMu7ajEZdGiSxLUzwpnspxSksP5gHU1elaajLeyqdnDnonIMeom8DtM9g05ibE4yFa1ecpItZCQZ0eskksx6/vT+CVQt7Kdx8fgctlS0UphmjTluAbKSo/vwglHpHKwPC56lOb1/Q4tRjyeoYtJLFKdG7zTUO33UtAdRVI2tlU4umxadjPVAvZvsZCv17hCyrGLoI3R0T/69uYpX99RRlG7lkVXTeu2WFKXbGOrVVUgFo0FPUNZw+2T8jR5aPCFSrEbafCFcAQW/rLGkKIs0mwlZkvAGZWw9TEyK0q3srHR0/YbrDjex/WQbpVk2PjpzYAl0RyKqqvHs9mpq2nzMK8uISmL3hX9s4ZV94Yh633v5EAcevjKhOp/9wsUxP79m1iju/e/emMda3UG++fxu2v0yn1xYymWnMbnpnhonP1lzjJ+sOcZbX17AmNzTL/iUZNm6wucP4NYaclZMzOky6zSfyYacZcyI88wUwJTcoY8eeTZhtxjISDLR6glSnGHDG5SZ+lB3zrv931/Bhyfa+PHqAxxv9qDRYdGiC1sE5adaGZubTLrNRJs3yMT8buVgRbOHl3fVYjHqWTktn2y7mYoWD2k2I8Uda6y1BxvZWeWgLDupa60Sj2Z3gGe2VqNq4VQJ+anD56MzUE5Z6NE07agkSc8APwbMwL2apm1JoOhC4HJJkjpjaT+kado78U7e8s2LeGFnPcsm5nHBj7v3yl/dWcueaifTi9O5dnohpVlJMQWgLR27M00uP0ebPMwo6V/oOdoY3k1oaPeTbc/jza8s4o09jdzTw0ymzunjKyvG4/aHGJ1txxRjcamqKvvr2oHww/F00WneBvD/3jzC3z7dvRjRNI0jDW4UVWNHZRsPXj2JoKJ1DfJ4rDvcjKyEK16zv4HJhak0ufzkp1j42hWTmFmSzh/ePcrtC8O7PIqqcaLZQ2ZH+O9jjeEFdUO7H6cvRKbdTGmWnf98ZgFtnmCX2Vw8Um1G7riwFFnVukykGl1+PAGF0kxbTC3EyVYvTa4AOin8OifFQlWbD19QptkdINViJNWiZ3phGuPywkKJrKjct2wMLe4g6zs0K0ca3Fw0PodPXTiaVneAv31QgabB1KI0nL4QkuRF0yTeO9TCpxePxhdSSbXG3hreVeXAF1JwB2QO1rm4apqGXiexalYRgZDMN1/YB4A7oHDn4tEYdFKXFl2SJG6/sJSV0/JJMutp9YRwB0MEQir+UHhH8mijm6un5bFyWjhfiU4Xe/FxsKaVCYXdC7Q5ozNZf6wZRVGjcsF0MqsknZMtXpItBnyKRKTYs+Foc9hsUC+xp7a9V9mdlQ4AKlu8tHqDvfIsrT/aRJ3Dz6qZBb12ZLdXhrVc1W0+Glz+fsfpYIkcPaaORJNmgw5/SEaSdPhCCqOzkjggq+gASVM53uTGoAtrtRraA4zOip5ay7PtfHrxaPQdv+GRhvDcUtEcDhfdV96gswFfSOF4kxu3X+ZQvStK6FlzsDuEuC9OtMdYyIpKRYuH7GRLr3vo35+az6/eOcDvbpgY9fnB+nYcHaFct5xsPa1CTyR/fu8kj95wZnZ7Xv3iYv6x8QRfXT70/qaJYjMbybDoCMgqU4tSOXrGWnJ28d7xFr40iHJbjrfw4Mv7uG1BCR+bWzrUzRoRvH/CcaabcEYx6nXcMq8ET0Ah1WbE6QsROSt+64U9fHHZOJ793AL217WjBySdhMWoIyPJQk6yGbNRzy9umk6bJxRljXS82Y2sargDMu3+EJ+7uJxbfaOwmPRda6wjHevh400eFDW8VolHVau3ax1yotlzbgk9kiTtBA4BlxMOSPAbSZK+qmnaRX2V0zTtUSDhjKNms7nLATndaqDNF97La3EHqGgLsP5IK7urnNwwp5irY0RNu352Ef/YeJKybDtTCxPTpswpzeC9w00UpllJtxnJSDIx5pJoG/jKFi/PbQ+Hy710cm7cTPE6XdiWf9PxFi6ZGNvGfjjISDLS2hEZ4zc3To46JkkSC8oz+dfmSox6iRd31vKpC/sPS/uxucXsrnKi08FtC0pZvaee3VUOVk4Pm/V8+ZldeAIKd/19G9u/tZxNlW3srnZiMuj45MJSZpem4zksU5hmJSNiVyzTbk44RnukCU2TK8C/NlWhahoXjsli7ujeiw0JusyLbB1lx+bYOVzvYtaodBzeIGa9jv9uq0Qn6fju1ROpaPZyqN6F1aRn7uhMjjd5uvxFLEY9v3zrCEcaXfhDKp+/qIzJBWX8fM1hfCEFpy/EzipnzLZ0snJaAa7NlSSZDSwelxU1idw8r5RHXjuEyy8zKS+5l/8LhDN556ZaaHT5eWFHDaqmMbs0ncI0K7KqUdXmYdvJNpIthqjcKz0pz4k+NjbHjs0UjgJWHiM3y9UzCli9q45pxWmk9TDV/MSCEt4+0IgvpMQMcXzV9AL+t72GSYUpMQWeB57bg6ZpHK538e2roi1kr56WzzNbqxmflzxsAg+EQ+R3yiApViOXjM+m2uHH4Q1hN+vJ6fCf8ssqsgY6JJrcAQKyijfUxCcWxA4rH5n0c35ZJptPtDIuN/msF3ggLBy2eoLUO/0UZUQ/4C4oSWNDx4JlICny1uxv6Lr/7riwNCpH2fxxmfx73KJeZWaVpDE+L5lmd4CrB+AnNdR89qLEzfiGEk3T+PBEC9kpVt464uDGOUMbWjZRphamkm634vSHmFKUJoSeBHnuniWDKnfT4xtRgQee38e1U3OxWkfOInOo+MLCM6PAGEkY9DpSbeHnRZbdTKT6/MVd9SgK3LKwlAvHZMeuALCZDL0sEaYWplHj8GMz6hmdlYTZoMecHD1bzy/LZGtFGxPyk/sUeADG5SZzpCEsSE3OH1iOyuFmKMzb7tU07b2I9x+VJOnaIag3LjsevIxnt1ZR4/Tx5/ePQ0hBAxzeIJ5A7PgJt8wfxS3zBxa1qbyP5HaduCOu5/b3HbvhU4tGD0uui77Y/t1L+zw+d3QGRxpdNLYH8AXDyacM/axMJuWn8r8vXEi908++WidXTyvgnovHdB0PdmhzFQ0cgWBXHwVllYCsxuxXWVHZfKIVSZKYOzqj35sqEm9QRtXC14z3+2vQlSC2U9ecZDZw4wXF3HhBMQC/fvswHxxtQdU0Wt3Brt/TH1K4oDSjl0O40xfCbAhrQq6cUoDBoGNCXirPba9GVTW2VLSgqFrc7/ORmYV8ZGb8beI1X17KvlpnnwILgDegdH1/RdW6vs8/Pqzo6B+ll2ZGJ0nodaCXdHhDkBqhMtLrdCyfGDZljOV/tqAsiySTgdExgmsUZ9h5+6sXxW3rFVPyuWJK7MVovdOP1vE9mjyBXseXT8pj+aThf/AVZ9i47ZKxaFrYLG3VrCI2nQhnRJg3OhO9TuIP645i1Osw6DRSrCYMOgmdJJNkNuAJ9J9mdkph6oATFo9kFFWjMM1GfqqVlB4+Ysm2bkVGelLiDrGR95+saMSQ+3thMRl4KEYwh9OBFPF/kztAafx1x7ChaeH5EPp/Hg1rOwgrOADKc+z874y1ZOQzLjeZOy4Zw7Siwc8HkfmSW31QeO7JPDi14UmKebZi1Ov40Uem8N3/7e1K/N4WCMVdA/VFdrKZT/SzPp5WlMa0orSE6utcW41EhkLo2S5J0k+BSwj79LzGAHZwBsOOyjae2RbeXVlcnsmmk22kWEzcu2zcsITT64sJecm0+0OEFJWZJWenu+alk/LYVeWgNCspphNyPFbvqaPdF+JAXTufv2gMuo5F9X3Lx/D3D06ysDyLogw7yVYzdnMreamWqJ2dSHZVO9nUYYJoNxuYOoAHwKjMJJaMy6bdH2J+nAg2M4rDob/1Oilu6O/bF5QiIZFpN7FobDatniDbT7ZRkmmL0tJ38oVLxrBmXwMLyzO7fFNKMm0sGZfN9so2HJ4gG4+3DPj7dPLK7lpcfpmD9S4+f1F5XOfB0qwklozLwuWXmV/W/f0vm5LH7iono7OTeu0mJFsMpFgMlGfZezl8j8u14/BmEpDVmNFdXt9XT7MrwL7adj67tHzIdiqun13M4QY3Ta4A37h8aHzeBoNOkrhiSj7VbV5mlaSzp8bJpuPhsWkzGZhRnMYVU/J5YXsNnqDM9bOKkJWwCefCMZlR/lvnC2Fb8Dwqmr3M6DEH3zArn3cPNSKrGssGsMu9fFJun/ffSMNs0KGXwvPX2NwzI9DqdBJXTy/gSIP7jArVmXYzKybl0ujyM6c0g4fOWEtGPk2uAJtOtLKloo1pRWm9dsATYdXMAlbvqWN6URqFGeeOxGPpeLZYDbrTovA62xiTY6c4w0pVm48kk55VM4uiTIsFvRmKJ8njQD3wccJKrruA39ARbnoo2FPjjIr5n2LRU5KsZ29TOEqYUYJt3+l7RyMekfX2F7b0nYMNnGzxsnhsFmM6TIJ0OilqoXm6ieyb+1eM4+mNFdS7eiePum56DvsafJj0OoKKyrhcOzfPHcXB+nZmj0pneUS4QH9Q5pHXD9Hs8nP3kjKmF4cXvjsq27jvbx9wsjuqNWaDhNmg52MXFJKVbKO6zYvNZOQ7V03u8gdJtRpZNrHvCGtJZn3M16t31/L1Z3cD8Msbp3NpnF2CWIvzTh579ygbjjaz4Wh3/uK8FDOyLNPqC4c57ulp8OCLezHowC+DzSgxuzSDVk+Q2+aPwi9rZCebkYC1hxrZUtHKvhoHnpDKwbp2THodealm3AGF7GQLb+w5ydW/reqq+2fXT+NQQzuv7KpHpwc5pNDgDpsg2o2QlmSh2R3AH+H/8K9NFSiaRF6qmXmlGeyscuILhjhY7yKghJ2WF5alc8cTW6K+S6pZj9mox24x8rMbpjJrVHisOn0hbF6ZrZWOXmGnf7lmP79ZWwHA9AI7L967NLpvOjRLQNQOH8CReher/rABWdH4+uUTuKOHueSE77za9b163m9r99fy+HvHAah3evnX3Qujjv/jwwqe2lTJuFw7v7k5Oqz8C1sr+PKzYR+oTJuBbd/rOzR9X+ypcXLZr96LeezXbx9h+cRsHJ4AhxrCvmm/ervbeGf13nqON7WDZKDdF+KmC4qZ0zF2Xttbh8UQdhTtqVxw+UO8uiec6WXltIKY5owjnTE5yV3zYiS5aXZmlKSjaTCtOHHF0Kwfdjvp9hwr47/9KgFFI91mZMf3Bjf3DzVJZgNzSjOwmfRn1GRx6f+92/X6TOUL+s6/V/PPnd3vxTIsPp078CaDLq55fH90Bo3Y3BHd61zB3+GU7JNVVjz6DnvPcEj4kUZ+qpmTreH0Eq6Awv3/3cX9/90VdU5ukoHLphUyvyyTyyfndSmnz1eGYmaeq2na/ZqmHdY07VBHNLal/ZY6Bdr9SpfAA9FJMQfC+AEkT3N6Q+yqcuLwhth8YmROLI+9eTimwAPw3K5GGp0+dtc4afH42VrRysu7anB4Q2w83hJ17vZKBwfr2ml2B3l5d3fKvZd31UYJPEBHtnaZP71fAYQjs7V6ghxucFHf7k+47RPyUrh+dhE3zCmiLML07Xdrj+ELKfhCCr9+e+CW4Q5vkLUHGtl/Mvo71rcHaPYqqDEEHggHgOi0DvGGNPZWteHwhnhyYyWtniCH6l08seEEDm+QQ/Xt7Khy8Ma+eurb/WyrbMMbVMm0m7lhThGPv18VVffre+t491ATLR4/Te3+LoEHwB2CWoc/SuABqHYEcHiDHGv08M7BJo43uznS6KbTkkpW4YPjbb2+izOg0OIJ0ujy87cPYoeyLfvmq1Hvf9ch8ADsqu0dPjzSlOLxdw9FHfvpGwfxBBQCssqf1x/vVTbyez304p6oY197rjsi14fHe99jz26rxuUPse1kG/vrooOBfOP5fV2vW7zDa9az/kgzWyt7B2no5C8bqthX46Sy1cua/Q2oqsbG4y00tvupbPVyrKl3nx6sd1Hr8FPr8HOwLn7dZyNTClO5YlIOY7Jt3DxIk4dI5dTOE40EOsJ3tkUkQD7T5KVaSLfquevCsl4286eLBSMkIWikwCPom9wUM+lWPXfML4mZUmCgnKtJYftOZHF+0urqf43V4JHZcqKVQ/XtNCRw/rnOUAg9fkmSutT4kiSdPi/9U+SRuYmfa7cYusIFj8kZmbH0r55dhKmPX7S1I/hDkytEToq1yxyv5/cZl2cn1WpEJ0lROyhzRmVg66Ek0Ot0mAx6rpwa3oEpz7YjSZBuM5IZx5QtHsUZtl4hqJdNyA5HA5Mklk8auL5QVlQCiorePPjcSBLh7yNJEgvKMpAkyEgKm8BJSKRaTeFwkDnhzMY5yRaSLUbmlGZQlG7j4rHR5k6lWUmk20woalhY0UvR17KadPTUxVg6Eo0lW4yMybGTbDGSYjVFnZeX3Ps7SoDZqMeo17F0fGwngxM9tGczirq19Ul9DSjgphnRt/tHZhag10lIksTCsr5/r9sWRi+Al0/obp/N2Fsb1Wm6mpNsYXRm9Jj9yIxu04fB6UoTpzDdSpY9/qJ2Yk4SmXYTFqOOKQUpPLe9ms0nWjnR7O1IJtzb/KQkw4bJoMNk0FGSMXxBGs4EP3plHw++cpCnNlez+CdxA3QmzIzR3WNuJMWA2F/bzuv7m/j4XzfR6OidLPB08OEI0YSfO95qw0/nuPnk37exv+7UFapnandPcHrRNFj1x039nmeSoCDNQkaSOa57wfnEUKijfgB8IEnSC4SV5qs6PhsyJhek0JkO1AD01OPOs3drN+5aVMqz22u4dloeDe4Qm47U09rhE/3alxZTmGLmn1sqmZiXwqpVK4HV3L+5e6JY8fN38YcU3n9gWVf9De1+Dje4mJhnZ+XUfFKs4XCBVa1eRmclxbU3X3+0iV1VDj5+wSjSE8i0rKgah+pdpCcZEw7xN7Uwlc7ErS9uqybJAsEIYd4CBInWzgOYDVCQbOCbzx5BBn66ajI/e+MgjR6Fi8ekM7Ukgx0nHaw7WE9BioXpJelsqWhhXF4Sx+s8qMDeR1ey8PurqfXDtb/7gNGZFjRN4uc3TmdGcTr6iNDhG4+10OYN8tKuaqpbfeQkGVh3rI0Lx2TyuaXl/HdbNcVpVg43evj43GLqnH4CssbFE3LIspuZNSqF94+2su5QI0t7BBTwBGRONHsoTrf1yhz80s4aDtY5CcbxLZeAkjQjjZ4QmgqfX1KMDj0v7a6nLDeFRqeHvbUe3EGFxWPTmZSXzK7qNp7bUonZoCM3xcji8iw2n3SQk2JmzqhUntlcxY4qB//bdpLcFAsmkwE9YYc3gL+sD+cx0gN2sx6LUcIoaVwxpQCj0cC7hxsJyjL+gIwSVGgOgKxopCdJ3HpBIa/saaDdG0AC5hdZ+bA6vL2dYTNy0fhMGl0yHo+fw81ebGYDl0/O5cPjrZxodLNmXz3Le/hVVLe5KErvFnQeWjWdlb9ZD8AnynuOHPj47Hye3lZHgZVeSRhXTivknqd3AnDv/N6+LeOyDBxuDt/BpdlpUcemFmfw721hM40Mrff+W7JJosbhJxiSe5mB/PTG2UwqOMr2Sie/uHFmr7JBWeVwg4vcFAvZyb0jBNY6fDi8oX4TmxYkm5hSmIpB0nhuR33Mcx65fhpBRSPbbiY3xcIPV+8nyWRgUn4KH51VSHWbj9o2H09vruTKqXksHJNNboqFu5eEQwyPtGRuiRLPVPj1vd39VOOMDlAhyzI/W3OE7GQzdy6ODrFc8ehKSh9YTSnwbo+FXI4NGr1wYdnA/Cg729hzYRhSVA7Vu8hJNg/Ip2LtwQYO1Ldz27xRUbusbx5s5Jb5pzdozUhiV8dvB1CWBOfW3uXQEjluXt5Zy6T8s9M3eLi5I05UzPMVWVVR41g56YHLJmbx8XmjmFaSTnWbD5NBHxUB83xF0mIsLgZciSSNBVYQlkne1jRtXz9FBsToidM07dpHBlzOpJcIKt3fb0o2JKdkUNniRSdJPHbLLKZGRKNY9n9rOdYStt9KtxrZ8eClOH0h/vTecbadbCPLbmLltHwum5zHX9afwOWXyUkxc8u83lEvKprd3P7XzciqxsT8FP58+wX9tvfdQ43sqHSgkyQ+sWBUQlJ5UuE4sj/xywR6Y2AYdOFdCJ0EOclmrp9dxO/WHouaoG+ZW8JTm3ubTCWZ9Dx913ymF6cBsPlECz9fc5gj9S5afb3NUZINENDCv5VBB3pJIivZTEhRMRnCmnGnN4grIKOTJP5462wmRTjp/mtzJfVOP3azgU8vHt3l8D9l+iy8V/ywl8DX73eXYmd6h7CzckAeaI2J0bm3cap3ZIrFgNsvx/ze0wpS+Ni8UdzxkWXk3/4rAPKAjRGLwJ7mET0XiJHH/3DLDC6f2h2BruyB1VHX7avs4tIk/vHZiwZ13TsXFPHda7uT8O6vc/LQy/vRNLhkQg6fWRodMrgzCa9RL/GpRaOjzI9aPUH+8eFJVE1j1qh0Ll26sKtvBsvKqfmMz0umINXC5opWWr1B7l5UxrZKB/6QwlMbTxKQFYwGHe997ZJewSRGKnPmzGHr1q29Pu/rt+vr2L3/2sHagw0AfPWycdy+sP/cMltP1nL973fErK8v+mrH63vrOVDXjkEnccei0Qn5VO2vcfKZf24Lj5uSdB7/yk1R4+ZMaNwXP7CaSGPaM6X179nXWW89yNatW+OaXp3PuxPm/LGnPG4i+/Wziww8cNXgfRpHEkPRN+cqs2fPoWVF3/sL37piPIvH5XQpnq7seC6d60iStE3TtDmxjg3VkzaPcGJSH90K7SFDVtRBmazISvTy0eEHoyXcPFXT8Iaim+oOdu8hBeTwMUXVOiRqDUXVuha8wY7knME4C+CArNJ5eV+8bYYIfvTKfrZUtDCpMJVsu6Ur+Wd/xJP0T5VOWVjVwOENUefw9TqnJUZYYSCqnyAcMhnCmolYuGToWupr3QNI07rL+GWVYChEi1fhk09s4aUvzicvNWziFOj4Hevb/fz41QOUZdu5eW4J/lBowAIPgNJHnw6XwAOnLux00t5HqNrdte1cG4w+Hnu/IjHqepjxDKR3Gtyxx09C13VG2yY3NDvY2BFhze0L9hJ6tlS0sHp3HdnJFm5fWBp1LKSoXSG/A6Ghmb7eOdjA4YZ2rp9dxIdHm2hwBTlc147ZaKAwzYwrIKOqGrKq8cbeOkqykrigNINjTW5e3FFDqyfIorFZrJiUN6Dw7T1RVI31R8PJhBeNzRpxmj5PoFsJ4vRFj8t4O0etTUPfjs75XNE0lL4mgAhcQbnrnvUN0bg5Var6P0VwjlM5DPeHYOShJbBiON7sQdEaqXf6UDWNrz27EwmJ2xeOYtWsaPPyWoePnVUOyrPt57RgNBTJSX9OeJfnf0AB8DVJkn6uadpjp1p3J26/PGAbYZsJ0HR4Q93LsJ9+bB4FqVb+uO4YY3OTmdcjvPE79y1kwU/fJ6RqrP5iOElYRpKJ62YVUZ5tJz3JxPyyTCRJ4qMzCzna6GZCXuzwx+PzUrjn4nJ2VTq5e2nf2su1Bxt4dW9dWNCodfHodaUJm1hMyE1iKMIqXDs9j5d31aMCmUk65o7OYXtFK65AkPxUC0UZNhaWZ3CkwYmqSeSmWvnDJ+ZELUzsJh2KBo98dDKzIsLWXjQ+hxZ3gBZPDv/ZXElFi7dLsMiwGWiNcDzPS5K4ffE4Wr0yQVlhcmEqLe4AC8ozueXPm1A1aPYEuP+/e3n6rvlAOB/Egbp2XtheQ43Dx+5qJ7NHpVPrCDBQB7OiVAPF6clsr3aQZNR3+UElVDbNQrWjezEu0b8gY9ITZXqXazdGBTYYDm6eV8Lnh6iu8XmDN8XoNHPrJNIEMBbF6Waq2gLogJ/eNDvq2AMvHe56va++t8vrOwcacfll3AEP+2vamVfefe/npli4Ymoere4gs/qIAjgQgrJKZauP/TXtVDsCaEBlm59ksx5vQCbFrMcTVEgyGzjW7KGyzUdBqoX/bK7k/SPNuAMybd4gxRk2JhcM3kPiQF0720+GZ4gks2HYIk0uH5/JW4daYh775MJR/O2DkzGP/eT6aXzr+b1kJhm5d9m4uPWXPrC6S/C5dE4Bhmd3IANZtsSFuK9eXMLP1oZ3pl++JdpUZtmEHDJsJvJSLb1MZOMxb3Qmn140mkP1Lj5/cTn/eKD72Btf6p049XzCCJyOEBN9Oe2fr7sCj91xbuzyCPrG208+uLE5Ntq8IXKSFWRV42B9O8ca3WjA7989zoXlWeRGuFG8ub+BVk+QIw1uRmf1TnNxrjAUOz3XAxM1TfMCSJL0KLAFGDKhxxtSooSeLy8fyy/fOtJ3mSD01DvvqXbS5gnxo49ORaeTek2Yealm2jsG0sW/eDdmvbcvLOQH18wgP9Xar9/NzXNHcXMCwRIK0qwY9TpCisrk/OS4glQs9Pqh0dy+uKtb39/iUXktwg7/WLO3V+S0Zo8rqv9mFCWzs9oFwPdfPoCiSfzwlf3Iqsb3r5nMf7dWcbTBHXbSlyR0mobdomdOaQZr9jd21XPBqDSunVVETnJvoW9heSZv7GtAkiTG53Unxsyym1k8NptdVQ5qHD7MRh0ZScZeoZgTodopU+0MLxID8sCigEUKPJDYzk3PTcDhFngANp1ojnp/+SnUVZTeO0HpYOlPV97QHt4ZUgG/143dnNZ1zBUnamEnflnFF1LRSZAZIwjBQO65RFA0QFEZk5uEvsNUFDoSxWpgkMK7PIqqopckTAYdTa4gRxvdeIIyBr2EzWQgLUZy2Fj8+u3D7Kh0sGJSbpS5barViCSFd03TE6xrS0Ur2062MTE/haXjEsuwuTuOwAPEFXgAsuwWHr8tphVCn3Temc3exHdY6j3dz4ONrVamRhx7blsVT354ksJ0K3++dQ6mBEMHf/LC2H47mfZTj8J1NjNyYuqdHwyFgDf2W68SUjUsBh0HH75iCFolGE5kpe+570ijlyONXt7Y19Dr2NFGN0t/9i5pViOLxmRxy/xRpNmM7Kp24PSF2HyihUVjz0B25dPAUIhyx4kWnjycmsVMv/Qn8MTj/YMNHG5w4YzhVwLgSSCD9dMbaxK+3ht7avn8P3vbv/dkfF4Kj906iwcun8BD105OuH6AllMwERpKOgUeAIdP5i8bTuDwybgDCj95ZTcHatsJKSo1jgCKqqESjljV6Ipuf61X4mCdi1j8/tY5fHnFWL552XgevHpqr+NXTsnlmun5PLJqKll2C4az1CF8uHn6wx4htE+hrg1He0+ow0WkgPiTt6LDYfeIpN4Loz7s42c26KhuG/57xmaQSLUZGJeXwlcvHc/4HDvZdhNWk45kiwFJp8Nm0mPU67lsSi63zhvFsSY3E/NTWFieyTcuG8+di0eTlUAAFH9Q5oOjLfiCCm/srccdkZG7OMPGLfNGcfPckoRNFrafbMMXVNh+sg01QfvZxv5PicsPXtzD4+sO939iB1f8dHAheTtzmQD8/r3o8fPy7jr8IYVjjW62VZ763vmcH6095ToEgkTZXlnHtAdf5W/vDW5tBBDquNf9w2jCLRg66toH/xzTgEBIpdUTZF9tO9srHaycmk+Gzcj43GR2VDqGrJ0jjUGtCiVJ+lbnH3CAcPS2H0qS9CDwHjD4O28YWX+iDbtRIsUaWwtnTGA77+IENZ/rDjbwmad28OreBsZ+69V+z5+Un8rVMwoHvHNT6xwZcddzIgJi6SVYEZGMtMkHKUaly29CIzzwyrOT2VXVnW8l1WKgKN3aZ0jwL14yjk/38NcA2FnZxh1PbuOxd4/x4s6wYKoMl8PTWYweuHpGQdRnt57Cfu+i0YMP335v758xYW6eE/0d8vtpxuIx2ViMenJTLMwp7R1VbqgxGPSY9Xp8AZkPj7ciaxo5yWYykizkJJtJsRjwhlRUYFJ+Cqk2IxPykzEZ9MwoTsdo0PPannqe2lhJqB//PovJwNSiVHyh8D32tw0naIpQJmQnm7vC7SfCpIIUJAkm5qcMeyK7BY+8xRMfVvLj145w77+2J1Tm1a9dOahrRYZsv2l2UdSxi8ZnI0kSuSkWphae+q7fDWcoefz5atJ1vrPqse20BzS+/+ph6urq+i8Qg847XagKzw5yYkQhTQQdYNRLGPQSSWYDpVlJTMpPwaDXsXBMFiaDjkkFQ2v5MJIY7HInUmpoAJ6JeD/kmbHKs+0syIW3B6BUPv7jKyj71mu9Pi/OSupyDL53SSG/eS+8QK54dCVyhznTmO+80XX+TTOy+cnH5lLZ7OTTf9vGO4ebePzdo9x90ZhedUfy2t7uiSc0ghffd8+28fi23nryOxeV8uWLx2JPMtHa7mPWj7vza9y3fCxJJoXH368h6A/gVyE33YKvLQiqitVs5LF3os3hatzwuaUl/GX9SUIdDj3bK1ujTMA+Mb+EVbOLOdHs5vW9tby+p4FlE3P4zEVjaHIFqGz1MDY3mZSIBG7Hmty4/TK7qx1dwR8O1IZ3ipQ4gRNOlf58T0YyCnDNjMKoz/4pw8ODrK/OIxHpDmnUQSjBbk8aG+3D0Z8PVJJJhyeoIgGeUPSj+StXTOMHL+3DFVBYOq53fqAvLR9HklHHnLLsmJHSTrZ4aPUEmVI4eP+ZSEZlhPMifHC8lZo2Dw3OAIVpZm6dV8yoDDtPbDhOICSTmWSizhmgvt3H6Cw7X7wkPK/88q3DtHnCJnveoEKqte+lyHdWTuKdgw3sqnISUjSa3YGYobkhvDtc0eJhTHYyAUWhqtXLuNzkrsSIi8dmc2F5VkyBZ0+NM27Y58HQHCGcbT7R2seZ3ciDnE9/f+scvN7wXGezRedCuufisdx1YVnCZm390Zw8/IJ1LM7VxJSCxNlQHeT6/IGXu3/FOJwuNxlp564T+7mExaind3ipvjHqws/Z8dkWrBYTdpORUEjm128d5L3DGSwdn8XOk228uruWR17Zzy0LRjG9JJ1su5lUq5HVe+ooz05i7uhM1h5sYGtFG9fOKECv07HxRAuLxmQxKjNs8h75nEnUT7I/ahw+6p1+JhekYDEObq4elNCjaVpXnDxJkh7WNO07g7p6gliNem5YMYu3/5mYJhDoJfCYDRJFaTYun9KtIe4UeCDaUTbSufwnHws75by+t4nDzeEh9uPXD/Ur9Dx6/Qz+t7MOv6yyZMzwOA8DWI2nppeJJfAsm5DDXYvLsHeEzDbooq/xl7VHmDwqgySjHpdfR0BR2VPfvePkCsU2H3z8vQrml2Wy8XgLigZVbX70UjhCXJrNiF6v5yevH0JVVd493ISiauyqcTJvdAYfnmjDH1I4WO/q8lmocfh4aWfYZGVGURqzStJp84b43MXhLYThkjXPVoGnkw2HT8UYKZqHX9zES1/uXvwmKvAAvLGnis9cNLbrfX8/lyeodp0X6mnPrGn45LDPjiOG+erXn9vNsUY3aw42MSHPTmlW99ZQkyvACztq0DoiFQ4FB+rcWEx+yjMDVLR4kVU43Ojlt2uPUZ5jp6rVizegouLn1T11uAMy2086uGtJGdsr23AHZBpdAWaXZpAaZ2e6J3NHZ+IOKFiNesbG2S3VNI3ntlfjCSjsrnbiCykEQipHG93cdEG3c38iOzyRc2ZfPLZqOp9/fhcAxanR5npfumQMP3/rKJIEf71jdqziAMwu7P4+c77dW5mVKD2FnUiGSuABeOLOBUNWl0AwEC6fNAiJB1g2MZf9dVamFYnUsmcDNrNhwEJP5/N5T70PepTeUeXkHxtP4gkoXc/ih1cf4MY5xeSlWmn1BjhY50InSXx6cYhHXzuENyiz+UQrKVYj7oDMB0eb+c3Ns9BJ8Oy2arxBhX217dy2oPQUv204H+Pz26qRVY1ah4+rpxf0XygGQxHI4BJJktI0TXMMQV0xkSS4fMrgbuRODj2cuEnE4R8NjYnA6XAGHJOTzGeunMCPXj04JPV9bmkZ37hiYp/nGKSw3X9Q0bAYwrl8EhEwNA2eums+V/z6PQ50+O3kpph56Nqp1Dh8tHqCoIUDMWqqhqaFy3zk9x921fHwR6ZE1Nd9UZNRx//7+Kzo6yXwfc9Hhios81DT306PXuoOJx7skUhJJ0lYO8xTk0y9p7XOoaJpfY/VRMKAJoJeJ2HUS5gMeow6XdSuo6ZpWAx6zAYdRoOu65pd/2uQYjGSkm8ckKmV3WzgmgQeBJF9ERmafrjY19I9N1U5owNOfGH5eL6wfHy/dQxnqHiBQBA2az2XzZrONSTgvmWj+dXbJ4amwljPAK37Yy1iCo5M76lp3U9NreM9khTz3FNtXuR1BstQCD31wD5JktYBXbFiNU27ewjqjsvhh1Yw7ntvAmFzo5c+UcrKf1T0Weapp1Zzyy0RifEizNv+eqkVVVXR9djV8Hq92Gw27r5oDP/ZWkVFi5f7lvW9y3O6uWtJ+aCFnqevy+Pjz4XjTkxKg3svKkVRFPR6PbIsYzAYcLZF7ww8/qn53PD4RgD8MlwyPpvdVS20elX0OshLMVPfHkBTwWSUCIQ0jAaJR68LBx/432fncd0fNqIh8c3LxnDBmCx8QYWtR2q5dvoEDtS14w3IHGtyMaMkIyr6SKd0rygKRek2rpqWjysgM22AZknTcg3sb5CJF7rCqoeQAjajjtsuHM36Q3XsrPNilGBCjoVDTX6CEZNA54K9IFlPnSusKTHrIJDgWi3TDFaLkTpnqGsnqTzTxLGW3lHJ9EC6TUezN1y5ruO6mqRjzujMqEh8AFaidTqXTI5WIHxxUXRy3Tmj0th60pFQu//xmRVR779/9SS+//J+AEb385NMKIgOD30iIov7d6/ovRD+6XVT+f6Le1gwJofLp0Yv7lfNLqba4eNog4vvXdM7GMijq6bx1w3HmVmSRll29C5IdrKZa6YX0OYNMrUwre9GA2kWPXnJJg42xdez3TirgOLMJMbmpeD0hpOf5qVamVKUyqjMJHKSTfxvexWXTy1kckEqhxtclHfszkzNt6OXwGzUMyYnvqlJrPmqLzRNQ5IkVs0q5ESzlzHZNkIqnGzxMiF/4CYtkbs8+x+6jEnfeyPmeb9bNziH29I0IxWO8M7bK1++uOvzXRHjZCjZsWMHM2fOHJK6yh9YzbEz4F9z7MdXUp6AD+lws/nby5j7o7fPdDPOOobCZPTOv73Jf+4Rvl3nA/9vgAJPp3lbWbaVJLOJJLMRCQ2PX2b6qHSWjc/msXePU+v0oSoqty4oZUZJOtnJYfO2V3bXUZadxIKyTDQNtp5s46MzC9BJYfO2JWOzuwJIrZpVxIlmD+NyB+/3G4ndbOCjMwupb/cz5RTSOAyF0PNix9+wcazR3eshN+57b2LWS9y7LJv/W9PYJfBYdOCPsdDsLP/tCJOM+6+cwf1XzqD0gdV8ao0P1oTNJmwG+OBbK1j06Du4gwp6CTZ8Yxlvf/Xi3hWPAH7yyp4Bl0k26dnzUDhY8Z8s1dz11C72O2DiD97BoIPy7CSONnqiEnWmWAysnJrHPz6Mjnz03tEmvnTJWH679iiyCvNGZ/Gzm2ZQ7/Sy4NG14cAFqsaz22p560Ajm4+34fSHmFyQzB83VPHfHQ28squ2K8D4bz46lao2H60emTf3RztyyYrKtb9dj8Mb5LMXlXPz3OgFe6Lsbug7Up+vQ/JoD6n89t1jXZ+HNNjTEDblK0630tjuJ6B0azpqXd27KIkKPAAtASAQbVoVS+CBsHldq7e7chWodimAQvO+BopSjVQ7u+uKXJrrJWjvEfHv6nHRJkeJCjzQe1elU+ABOOHseXY01Vtq4bruReaq//de1+sfvnaIO5dGKxe+99I+PCFYc6CR+jY3eendk2m908eb+xto98usPdjEDXOiE68Vplv57lXxIyP2FIT6QoM+BR6Ap7bWYNTr+PxF5Rxr8mAw6Klq86JoGh+ZUcgnn9jM0SYPr+9v4hc3zmBHpQN3QOZ3a4+yv66dvGQLf7njgrj1r9lXz5MfVJCbYuHhj07BFmN3K5L3Djexo9JBms1AoyvAhqMtJJn03L2kjKXjE89mNbUwla0xFmZmg57vXz2JNm+IyQPQFvec1yMXfZ0CD8AD/1zNo7cO30Kuqx3/qeWmmVn85KZ5p1TfmdpLPZVEtkPJo//beaabcFaSqMloX3x6XGKh6QVnN3tqnMSyfzr8w8swGaOfB05viGe2VaGoGh+dWdhnHsiFY+M/D26d373eWjE5jxWTuyO2jOsRHTQ72RzXr3SwFGfYKM6Ib6KcCKccqEPTtCdj/Z1qvZF445jjBBSN/1sTvQsRS+DpyegHVnPJT96OKUwBeGXYVeXA3eHYo2jw363xc030xY7KNqra+guoe2r8fn3lgMu4ggqlD6ym9IHV3PXUrqhjsgpHGqIFHoB2v8y/t1Sz6WT0alZWYMPRRoKyhk7T2NoR8vW3bx/t2toMqdDidLOvxkmzJ4CqquyrbScoq5xo9kRlVHrkrUPhSFRoUWY3Dywv54OjzTS7A+FM9jHiz59Oqtp8BBLM3j7UxBvmAVmLEnh6omjwzI7osOuX/nXwwRa/9fyOQZd9r8f77TWxQ5V34onYWnt8fbTg/f6RZo7UtVPd4mXN/t4R80OhEL966yDbKhJzlu8Lp7//Ja2qhU2yXtheRWWLh6pWD83uINUtXv6+sYIjDW4UVaPFHeTNvfUcrHOy8VgTh+pdqKpGfbs/buh2CAsxsqpR4/BxqL7vfoPwA1LVNDYcbaHe6afJ5ccbVHjvSHO/ZRNBr5P4/sv7+fXbR7j7H9uGpM5I/r23+3V/uzybK1r4z+aBz4md/GdH4n1yyU/epvSB1VRWDv56Q8lICWTw/L74eZsEQ89VE8OCTrENVqxY0c/ZgnOZ2d/tveN+osWDyy/jDSocbeydvPt84pSFHkmSbpIk6YQkSQFJkoKdf0PRuE7s5qFzMIWwpvZ4m59lv1gX+3ommDUqndSOKE8GCW6bN/Adhac2neTR1w7yzed2c7LFcypNjsuemn7U6YNAT3xHZo3uJJGdGHSw6YQz7GCuhUNPP/zKPt46EC2UHG31owIGnYSiwbSiFDQ0TjRF34RXTMlifG4y5og8O8lmPXqzkUVjsslPs2Ix6rlq2qn5eZ2v/GHd0f5PSpC54/tO0DtczM6Nvu6aPbX41bCG/e39vQM1rPztBzy29jif+Otmjja2n6ZWEt4JlFXMRj0GncqJVi8v7eze1dSAD483sf5YC28fbGZaUTh0aHG6lSl9+PMsn5SL1aSnLCuJiQnk35lZkobJoOPi8TmUZNgoTLOSajOybELiuzx9MdyL7YenRbyeEv+8TcdauPvJbTy8ej9ffDrxwDeD4bKfv8PxtvCu75LHBr7bPhyIkNXnJ7+9fQUVj67k/e+J3/98xwVc+Ei04FOWnUSm3USK1dhrR+Z8YyjM2x4FrgJ+BNwFXAxMGIJ6uxidZWfrKdhxW3Sw9XuXYbcY+qyj84FR+sBqpn5/TdRng6G6NWwCE1I06hz+rlB+mqaxp8ZJUFaZWZI+YkwSJOA7KydiM+v52RuHaPHE3zGoeHQln3liE28caqanj3G9082f1/deWNqMBgpSrXgDMs3uEHurnKTZTFhNelJtRswGPVdPL8Col/j1sok8/MoBdlS20ewOYtLr+O3bR3j0tYMsKcvk/munnPI25/lKewJJeBPl+Q+auX76kFWXMLsbg0Temfsbunc7Yu2CtbgDqJpGSFY42exjTM7pcdj1y+EHzoG6dsxGIyZD751Bv6KRbDbgCcp8ZcUUZo3K7DN62vEmNyaDjt99fBZJ5h5mDL4Q+2qcXWYAFc0eah0+phWnsbC8O5T3l5aP61ntGSHR+fXaVZd2vf5zH3lM99c5u4JGVLYO7w778T5MHJOG9crxGSk7PYLTy5/fO8Zf1ldw+eRcHry2D61AH+yvdfL+kWaWjstmQr4IaDDSmZifgiPOsRqnHHcu+OEr3Sboeh2kWY2kWQ2MzrKTaTcxtSidSybkcKLZw8kWD1ajntKsJGYUp9HkDrD5eCuSFI4W2mm+drTBxTuHGkmxGDEbdOSnWpk1Kh1TArkvTzdDIfTImqbtkyTpADBO07T/SpJ0EnhoCOoeEj65qKwrN0emTUdLhz9EihHaO9b1f74jdsSyU7GxvXVBCbKqkpNiZu7obqftI41u3j7QrY0+lWSJmbah+AnD2M16fv32Ef5793z6EsPu7nB8f+NQbDOQBnds46tVswvJsZv48HjYxMingM8VxGoIJ8W6YU4RIUVjYn4Kj609RlWbF1XVSLMZCYSULrOitUdbmDYqg20n27hrSVnceO0lGVaGJgDxucUnF5Tw/b8MTV01Df2bVg0HY/OifXAMWt8mZysm5rJ6Tx1ZyRbmlp2+HCoSsO5wEwFZRdXCwk1RupV6h7/L7+PTF47mpd11pFqNvH+0FbPRyNQ4YWOdvhAv76pD1TSa3cFe0dre2FdPTZuPbSfbuHleCS/urEXVNBpcfj46syhmncPN/Zem8Is1YSXIYNU7U7+/pmsefveh+AqwT8wv4YNjLdS3+3nw6kmDulai8/2RCEVcmkUiMiXkPrHjIjiNPPr6IWRV48mNJ/n8xWPJThm4L8VP3zgUDihU0cqfbo/vTygYGbiHQHmpqNDiCdHiCXGs2YfFoGPj8TZ2VjoIKirHmtwYdBJzRmVg1Ov44Fgz7x1uQkKizunn04vL8AUVfvPOUY41uXH5Q2TZzYzPS8YvK1w0AH/R08VQrJiflyRpEfAc8B9Jkg4B1UNQbxSH6l08smoq33y+24zgjvlFPPiRsJo5nkPs0UY3Wypa2VLRygWlGWz73vCHke6kMM3Gt1f2fvAaOrS4qqqx9WQbJ5o9LJ+YS3rSwB0Qs5KtnKqxTsWjK7n0l+uoc/jQ6SSSLHrml6bzyr7eZkKnsvP14NVT+PP7x3p9npFs4i+fjJ5kjXodBp2OonQbU4tT2VvtpPl4tD+GTidFRkbshTToJdbIQwJMBomAfGo+RMlmA9fOLOH7Q9IqSBlClbZRCptHxiMypLW9xw5HVmoSJ53OrvN6snhcDrmpVox66bTurJoMEjaTgZASAg3yUq18efl4Pv/0Ntx+Gb1O4qOziplblsUb+8K+SEZD/PbpdVJXiHhjjO9h1Ic/0+kkDDoJvQ5UJXw/DQUBWeVfmysZk2PnggSVNVdOncZv396ArGp8bmnZkLTjuc8t5HCDizmjoiMAGgyGuAs2d0Dmzf31GHQ6VkzKjVKWDHZeiyyX/tf7kYBU69ApogSCxOgOd28e5K1u0uvwoYxI7bwgzB/ePcbhBhcfm1uMJMHnLyrnsXd7r6kGg9Txp9OB2Sghqx3vpfA6y6jXYdTrOt5LXc8USQKDPrzakjoWZLqI46eLD442U9nqjbJoiMUpz86apn2j87UkSVcDE4E1p1pvT7799/c41AqFydDp7/zExmqe2FjNsR9d3uv8Iw1OPvHL9bQBQWBZHky953IsRj2aprHzRBPfeWELSSHY7IBCYEOMB1/kQ+2iB1ZT0fGZw+PHoDfwzoE6rpnZHSmqzR0kxapHr9fT5g6SZNJjNOoIKipmQ/ghm2Y1sag8A4dfZm+Nk+qgwhf++QEPLS+mvLx8QP3iDkRL+z3DEydCp8C4fFwK+w63s/inYV8nPeGptHPfxgh86nereacKrp+RxqQ02O+IrksCZuXBtg5f8lSzjvaAyu9unkZFYzv1zgAXlqWzo9JBkhEK0m1MzknG7Zd5dU8ll5RlkJWZxm3zR1GebWNMTjLFGUmcbPGQ9O4RPjjeyjXTc5mWm0xZob2rTwFkWcUvK5iNeox6He1+mfRB9MdIZEaugR39RJzrj7FpEp9ZPqFXLoaeC77f3DyDe/+1M6E6H7tzadT7N+5bwmW/CocouKGfodzzujfPG8XfN4YDhqTGWPf/5bbZfOrv25hmh8unFkYde+7zi5j8wGo8wOovLO5VdvmkHOra3Fw8Ji1mpDNZVgmqar9R0AAKkiRqPfGlszy7HotRT0Gajcsm5zOpIJW/rj+O3aJnenEqJWlm/t/NM/n6v7fw1SumkGozkmozdoSUhgl5KfiDMjqdrmsBomkaIUXDbjZw4wXFNLYHGJ+XjKpqyKqGyaDD6Q2yfGIuJ5o95KZYMBl0XD+7iEZX+FxN0/AFFSRJwmLUIUkSmqbhDykYOh5o/eH2h7oUTwd/eHmX4FDRh/nxvU/vItgR8OPP60/wtcv7zgMWi8ixElJU3jvcxK/fPsJ3Vk5kbG60jfor+w6zaa+bH94Unbdrd7WDHz6/iyQTlGRcyPTitB7fTcZm0g0oDHgk3pBCKuDwyby8s4qrZxT3W2aoGSkhq1+9dxFX/mb9mW7GWcd9Fw9OKbBqVhGrd9UwZ3QmtgSTGffkuysn8cSHx7lz0dAoJoaazy0ZfaabcEY52eJh7aGwIvo/W6pIsRi5fEoemw5Vsa1u8G70diAzGaaWZpFmMVKWm8K0whRa24OEOnxH81LMjMu1k5tipjTThgRMLUolKKtYjHq+euk41h5sJDfFiskgkWI1MrUwjYCsoHbkXAznrtMllPg6UTqfi76QwqYTYaX4+qN9B6I5ZaFHkqQc4FtAvqZpN0mSpAGjgd2nWncne2qcNHco+V0xrGnKv/161HubUWLFL6Mn3Lfq4aUXXuej113B/IffpMUXvYCsoduULZbWL/KBXvrAatKsBhwdddz7n91UPLqSh17exxv76slPtXLR+Gz+vaWKFIuB62cX4fTJzCvL4FC9ix+tPgDAXYtKSbGZ+cnr4Rw7y/50kDvnuvnuqsSdJIx6HZFhBU5lgf/W4eg9o54GQyHgnarw62d3OmLWodEt8AA4AyoScM+/dkdo6fWEVGjyaTT5POyq9fD0zu5CV4xPZ2e9FwmJ62YVcv2cYuaUZmBePoGWF/fw7y11/GtLHZlJRn5x4wyWjs+h1R3k68/t4mijm2lFady7bAwuX5D4gRnPLk5V4AE44tD46rN70ev6DgySqMADcP9P1/FMxP3SKfAAPHMM/q+Psj1NRzsFHgBnDJniU38PRwXb7YbvPPchD1/XnfX+wh+/RmeokCt/+36ve3jqg28QVDQeBv53z3xmFGd2HXN4g3zz+T04vCFuW9h/wJK+BB6Aenc4fHhFW5CtJx3Iarfi4JntdUB3Tq0vP7ObkqwkrEY9P3kj/Pm1Mwp5YXs1Rr2O7101iVGZSTy7rZoah48Lx2Qxd3QGuSkW/CGFf2yspM0b5FC9i321TorSbXz98vE8sf4Eu2uczCxJ4/MXjUFC4vfvHuX1fQ1IwOVT8rhz0Wie217Nq3vqyUm2cM/F5f2G7q51+rvCpE747utRfpDxqIiIQjfYncov/fMdfn3rJUB4p/zXb4cjDj68+gCfXty9SLv8529zsCkcXOAfO6LH18f/tAmA1hBc+7sNUcf+u7WK57dXk5ti4ZFVUxMSfvvCV7UbzoDQM1L8Q4XAMzh+tfY49102cKXAf7eGjWvWHW7uypMyUJb+bC2qBn/bcJLjj4w888zfv3eCb1w5OJPVc4HsZDMZSSZaPUHG5SazToIbHtswoNQYsXADbhec3NMpLHQb6iZb9MwtzUTVNJaOyyakarx7sBFPQMZq0jMhL4WrpuXzn61VfHCshVEZVi6fkk+zO8i7B5uobPWyvbINWdUoSrewdFwOn1hQ2stSYzAoqsaz26qodfhZUJ5Jpt1EiztIcUbfwZWGYv/pKeAA0Jlwoxl4oq8CkiTpJUn6iyRJH0iS9PAQtCEKbxwbma9vhwdf2EOrL/4Css7h4+drDrGtooV/fFjBWwfqcHp7e4bIPVKYv7Cjig+PtaBpYQfaNfvrUVWVJleANfvq8QRkjjS4eXFHDaqmoWoa7xxq4o4LS6PqeXLzwCwD/aGhc0ofLiIz6QKEZIVQHyng3zjUhtMbxOELsrvayYs7ajjZ4uFwg4vjzZ6uulx+uUvzcbC+nUZXgJCiUtXm5Wijh1O0BDtn6RlV71Scn7ecamMGyT+3RJs61rT3PfMHI0KLP/5udLjrQ/UuGtr9eIIym0+cekjrqOuq8cOLd/LI6n2sO9REIKQSCKm8vLO6I7yozPbKNtxBmRpHWJ1xOMKHqsUTpNUTpLndz7aKVkKySp3Dx9aKNqodPhRVo7rVR53TT6snyPEmL26/jDsgc7zJQ327n3014bDxTS4/x5uGJ8LkUIQTeHFvtzpn9Dfj72R0CjwD5ckNe9l4vJUXd9ZS2XLqLf76hlOuYlCcrYEMOtMnxPoTDIz1B2sHVa7zkdzHo1lwBrGZDNy5qJQLx2Ry24JSNAaWC3AwePwKJ5rdeIMKB+tdHGtw4/SFaPOGaHIFaPMG2VPrZF9NO5qmUd3mZ29HROFd1Q5qHD7afaFw8CpXkIb2APXOobG9cftlah3h+f5Yk5ub55Zwx4WlLB6b3We5oRB6ijRN+yMdGwOapjUB/Rl7XwFUa5q2EJgrSVJhP+cPGU9vrcbah6L743/exN82nOBjf9rEz984yNee2cOtj/d+gul6OJM89PIBVDQ0TSPVaqA4zYJeF97Ka/WE2F/XTnm2nf/P3l2Ht3UdDh//nitGy8wUO8yJA01SZli3bu3ateu6Djrmbus65r6D37jbOuyw69a1a9eVmZukYSbHiZktWax73j8ky5YptmOKcz7PkyeyLujcc+nw+dSFs7GaDFiMGh85p3JAJ/zH3j+6ge8ausY22/lUmpPrZri6Bkk846rH4qPc/X3jMX7+9EGsJo31lZkYRbwZXVGGjbevipemrijxsLggjUynharSdJb1a7qi9Jqfn9ocaN1J7OuZT58z5m0/f8HomnL2dUO/Tc+uHH4YTlOfJ92nz02tzSnNsqHrku5QlJL0yR8RcE5uGm3dIbKcFrKcZnKcNgLhGBajgbPnZuO2mlhSlIbbZkrpR5PntmI3G9hyrBODJojEJAsL0rhwQS7LS9LJcppZUZpOSYadHJeFtbMyKcmwU5Jh54yKTIo8ds6Zl0Ou28KCAjdLhhg8YSh9a0r++K5l4xUdg+rbc2f3V4aeJPpjG8Y2WfGu+t7UwwfvfmVM+5gOvjsFIykqU69nVg8BbJhXMOy6yqmpoTPAZ/+1g39sPMan/rGVAw3jP11Jf4XpVtbOyqQo3c7Zc7JZV5nJrGwnldkOFhS4qch2sqEym4sW5uKymlhdnsH583NxWY1cuiifpcVplGU5KPDYmJ/vZmGBe9xG3XXbjCwq7H0vmgwaHvuJ+8WPR4/LfUKIiwEphHACHwaOnWCb9UBPEc6zwDLiLcwmhc2m8f41FZRnOfjkP7YCYBHgtBk50tJb2tkRi2E2SOq6UksPN1Rk0OQLEQhFk6W4MV2SYTNz4bxcjncEMAhBW3cIf1jHrAnm5mZRmG6jPMvB7m+k9kGa7nMrxDvRQ2gElUrGRCfrTKeZUFQnGtMJx3R0HTQBGU4TgUh0QOn3JQtzWFzk4edPHyQU1dEAl81IMKJj0ATd4SgOi5H3bKigOxhj49E25mY76fBHePVwK6vKMvj2WxdPwNHPLGYDZDhSR/Z5+ST219DVTXnO2EYz2F439oe2Jack5e9rVlfw/MGtSGDZIIn38iwnh1t8mAwaYUPq8UspODsxyky+Z2LnHbKbNHRdEuxT81TXFaSjO0S220q2w8IbNW0cbummrTvEoYYudute1ldmcf58A41dQT7/r220+cPcetFcVpams/loO0aDwG01sbIknUd31mMUgupWP4FwlGAkRnmWgzcvL+DqqvgIbtuPd/Dozno0IfjYebMH9IuBeH/BjdVt5LgsLCyIx+niwjQ2DfK8OtQyttqRjYdbufmPG7GaDLxw6wZstsHj3+HuLSY55h26/fpnrljEZ64YfMjekT5n6zqmf+35ULZMk+Eq37Qoh4d2DhwIR5kY939kA4/tamBNeeaJVx6CNfFsGmvzuPGyr8FLXUeAFf0GKTnddQYiRGLxlFObP0zoxHNkA/DuNcV87aolJ15xhK5cNrCO4hMXzJn0aRCEEFy4IHfU241Hpuc9wI+ALKAGeBF41wm2yQTaE5+7gAGpFCHELcAtAAb38NVVo9Xq01lcmMYbh5uT34UkhPwDX3YxXefcufn8a0tvO8eN1W1E+swuWJblYFGhm7PnZLO/MV4VuOVoa3IG+eMdQS5bkk9Z5qk5r4wEYiO8waSE0kw7s7Kd7KrrpNkbpSd9JyW0eSM0eQe+mR/d1cQrh9uQiZu6PMuOQOAzRDEZNN66vJAcl4XHdjXybGIW+Ud2N1GSbsFqteIwG4cc5lfpFY4xotKQkbr+txs5MsZM+yO7h+9wOJyNR1JnfP/bqzXJZo+DTdjb7AsR00EiafWGoM+8tj198Nq6w6yZNfZEw0j4IwPbI+w93oIvqnGwqRtNg67E0Owt3RE+9c8dXFNVjC4l58zN4bcvHOZ/OxsQwNcf3MXd713Dm5cV8LfXajAZ4O+barAaDRxq9hGO6uyplxxs7mZenpt0h5lVZRnUdwZ4ak8Tu+s6sZgMzM5x8V6PbUA76+f2NSeb0+W4rMk5GQZzwxmzuOOx/URiUOwZeU+699y9EV84hi8c4+rfbOThj5816HrHu3ofQLEJbn/zxGfPmdD9T6R7d594ncmgMjyT62dPH6StO8zmo+1UlWWMaQS2920o5387Grh65dQMbQ/Q6Y/wyM56pISOwLjOcX/Km5vn5l1rS9l6vJNbziznv18f2XZ/fO0Yt146H6d1bANczDTjNTlpCPhP4m8JfF4IUQs8IqXcPMg2XqCnaNENHOi/gpTyLuAuAEv+7FG95dxWAzEdMhxmukMxZuc62Xy0LTmJpgZ85cGdyfaAw3FZjVSVZqRkemwmDT0cD5LNYuDm9WXceEYZrb4Qx9uDVGQ7ONbqo7k7nokyGcSIh3edrkS/M7CsyM3W4wMHyy5It3HTujIe392I0aBhNWoEojoGQXKkNV94YA5KEB/2MGbU8NjM3HnDCr7/2H66glFKMuxcsCCP+s4AQvQOXSwAqzWewLKZh++cr/QqSBu/4R2WTtHkzgv7jUBXkN5bQ9AzbHNfaTYT3aEomiYG1HQBLC+ZmFJFq4Bgn3unJ2R9b6d8t5WarhiRWLxW06DFSOT9cSdGYuqZhDTDYUZL3ABumwmTQeOyxfnUdwY50OSjyRtG08Bs1AhHdTQhMBsEJoPAlmhKazUakiPpmAzxEeKMg3SAtyfazJgMAotp+ESUQROYjQaisVhyu5GwW4x4E0WWhWkjq2XLsg2daa9u7eLi/3uRiC65dmUR3x3hzLkfObeSTdVtmI1aMp6U6UH16zmxR3b2DgQ01hGnb714HrdePK7zyo+ayRh/LoWjIxtJ83Rzy9m97brNo6iRm+zho6ez8bqqyoB7iY9q/E7gtcT39wohviOl7D8V4mbgfOBV4l0KfjXczhcXpjGaMuEvXDoPiSTdZokPX2zUmJ1j48qfvkxU6liIDcjw3FBVQIs/Sl1XEJsm8IVjLCtJ581LC1lTkcmRpjZ++3I9SwpcfPLiebT4guyu87K+MpO1iXHBM50W3rG6mK5glJvXl/GlB3ZwsNHHnTcuT/mtYCT+kh9qUs3RyB3DJGTDyTJDyyAFLH3rZtwWeOCjZ/K+3zzLk4fizQHftCiLzpDkx9cs5vevHMekCQrTLCzId5HrspLhNLOkyINA5/b7dtDg693jnBwHHz23goJ0B8/vb+as2VnMyUvjG29exPbjnaxJTCaZn2bj2lXF5DrN3LPpOO8/s5wlRekIwaDtRC2DJH5PlsbAjul954/pz0DvKHgXzE7jYGuQ6rZ4PyyzgAyHkQbf5DWn+cJFFSzrl8Bf4Eld56w5mTy/P16TUnqC9rc/eH/q8NBWoyA4whEkvnBJanV4VamHTUc7RrTtyn41Mh85dzbbj3XQ4gvz1cvnDlj/Xx9cww8eP8CZs7NZWDjxNYJui0ahx84337KIz/1zG0faAiwvcLF6VgaBqORfm2vojoBZg1svW4wvFKPZG6TQYyMYjfGbFw4zN8fFJy+aiy8UZVZWvAnhzevLyXNbafIGuWF1vImfEIJrVxVT1xEkHI3REYiQaTfyj021zMlxUZxpp8BjpTInnkNNd5i5bnUxrb74jZ6fZh30WXTW7GyKPDbSHWbcfUoJu4IRHGZjykhhz+1rojuReTk0igERXrv9At768xcp8Nj4+TtXDrle36Zp7/r1s0Ou980H9xJKVC3fv61uxJmej51bwfMH0qjIcZLjHnmhwB+fP8D/djVw74dS7wP3FOWbziuFp4+eeL2J9pmLZvPDxweUZSqT4N+bj3H1qrH1bZtqdrORd6wuocUXSj7zlF7RmE4gEsNlNZHpsgwYYbc/pxH+/L41WEaZ1ozpku5wNOW5P1OMR6ZnBbBWShkFEELcDTwtpVwrhPgj8CTQP9NzH/BXIcQLwL1SyjEPmfTFS+bw7Uf3J/+uyLCwp8HHvgYviwrT+PIVvUMcvv7liwYtNcqwm/j21fGMSVt3mHs21hCNSd60tIDyxI33hStX8IUrU7d72yDv6By3lZxEIfQnL5jH47sbeGF/O0XpLpwWI41dQf61+Ti6LnnryiIKT7IPQWNXqG9LHVaWeNhc0zHi7Tfduob1P3ot2T40JATnzs2iriOANxihrnPgQAldIfjiv7fx05s28LfXaugIhIlEJbNMBrbXd3O42UdHIEKOy8LK0gyuW1VCmr335vm/q01c/8fXkn8/3qczfFWfGrF8j21AH4v8NBvvWFvGO9aWnfDYQrHRNYNxW41k2E2UZtpp8oaobvUnm9L0jP4lGJjxMSRm9Yr2+TLDbsIXimIyCC5bnM/58/Pwh6N879F9pNmMVOY4WTsrkwy7mV8+e5CW7tE1xu9b22U2arx9VSGbqjs43uYnEpO4rEaynRbcNhOXLs7j7perafaGeHRvKx84L3Vf/7sttXnaa/t7m44dbRvYV6NnckyAPHtqCZLNBCOdKDo7I3XFhXmuZKbHYx34aMp2mmn2hTEI2FCZOtNzjsvC21eV0B2KMa9wYK1qlsvOHW8bn17eBgGZDiNNQ2RWjQBCw2Uzcf+WWtoCUTx2E9keO26nlVtWlfDqkTaOtHSTZjNxRkXmgLlhLl3c2xm5b6tlq8nAVSsGNj9xWU3MzUt9QX29cOjaqxyXlRzX8Il7gyYG9PXxBqP87oUjFHisvL2qODkZ3fv+1FuhHx3liEL//uiGQb//2/vW8NqRNrJdFqIxPdnX4NHPDz0n0LWrinh6XzMSmJMz/PDbfVnNRi5amDeqcH/6nk38e2t8JMT+4Xn7FE1o//sPDR03k2lfbcdUB+G0dapmeHpkOMxkjGGi9pkuEtO55/UaWnxh1s7KxGE2DDoxvcME168p56b1ZRSNYWCemC75x8ZjNHYFqSpLP+FoaKea8cj0pAMeSFbGmIEiACnlcSHEgFiXUoaAq8fht7l383EOfvtSPvKn11lRksEHzp/D9b95FUgd3nUoHz23nFsv7s0Y1XcGCCXa3de0+dlytI3ddV188sK5OAdJiA2npq0bKeMdglu8IZwWI7UdAcKJVMHxNv9JZ3r62zeCY+7rvu0tKR3ivCHJnTesJBCO8dX/bKduR+Og2z2xp4kPnjMbbzBKMKxT1xFgdq6LPfVdzM51Up7lwGIURGI6er82+OvmZfHoJ9bwt1eO8423LhvtIU6Y2TlOrCYD8/LdLC02cOXSAtxWA2+982WOJzJ/MUg2L+oRlfDmJQUEgkGMRo08j4NXDrUSatMBSbbLwiWL8vjCfduTk3VpAq5aXkgwovP47nraqzvQJTjNgmUlHl442D5YEAFYWuBibp6LbJeZLce6WFzowm6x8OO3l/KPTcfoCkRIs5vYWetFAluOttGcKNWvaR1YCt9/vpwTjQfYt/7sYFuYZX3SloFR5N3+9GwTb13SOydF37mzBiuZ+uSFc9hc3UqOw4K9X/8Th8XIzevLicT0calBHU6Oy0LnMO3NdcBhjo/QuL22k5iuE9Ul9Z0B5kXcNHQGKc2Mz81jNgr8YR2n9dRofhCKxh8WdR1BQtGRxfVwNaHDOdDkpdkbwh+O0h2OkWaLx9EdD+4YcpuLFhXwym0Z1LT5WD1r+Jm5T9b/dg7+bASo65rY3x7KdMjwADyzv/nEKynjpvqOy/nwn1/nyxdM/txQyuTwBaO0JN7jR1u7SX0T9+qOQENXgOPtgTFlegKRGI2Jwbv2NXhJt5spzbTjspo41OxjV20n58zNxj1MM+PpbDwyPf8P2CSEuA8IA28D7gYQQqyAUbVMG7XLlhTy/IFmfn3z2uR3b1leyIsHWjh3bs6A9a3G3pJojwUe2NrAqrKs5OhNlTlODjV3E4zE8IcifO2h3Ugp2dPo5a/vWztgf8NZUZpOhz+Cx25KNr+an+fmaGs3us6ENLP52DmV/N+T+4nqksEqOnIt0NgnVfuB8+bx5N5mNtbEywzetboAm9mAzWzgtksX8OyBFrzBGBajYFmRi9equxDAT65dSmFiGMK27hALCtwEIzrrKzLZVd/F8fZu/vxKDbqUbKxu5+73rE4Jx7z8LL7x1qlJGAzGaRasr8wiL82KLmFunovKRCn3t65azHvv3kRMQkW2DYvRxP6GruQ8QBZDPCN0yzlz2dvoZX6+i9nZLv7f4/uwmTTS7fEmiO88o5R9jV10BWO8ZVkRs7Kc+CNR0h1mnFYD0RicMy+bq1YUEYod5PUjHQPCaTYK3nNWBRkOM0db/SwpyaTZG2JhgZvKXDdmo4F2f4AMh4WqsnTqOgJcu6qEGII3jrZz8SCl2fv7jSZ43Yp87nkj3oct1znwEVGe7eBwczcuq5FFhZ6UZbddMoev/XcfAIvzhy9p/8st61P+/uBZ5Ty1p5FITHLF4oHhXFeRlRwoI22QWcfj/WEmNsNjFPCO1cVk2s188cHBe42vKE0DNK5cVojFIPhJYrj18+fnUp7tpCLHwdtWFPHwjnpWlqaPujBlKrksJrJdFioTBQQ9tnzpQpZ/64lBt7lyaTb/2RZPBI8mbxfV44VQWS4Llj4dFW67cjG/erlmyO3yPFbyRjGYwlg985EVnPGTNwCw9Duub4xigunxdOS7lw07j9Fk+dXVc7nx7/umOhjjatgJeMdpBNZ7b1kz5m3vvHH1iVc6hf3hpqGbv54O0h3xKQiOt/s5oyJzyMEq4v2jNRaNMX3ptBhZXZ7BkZZumrxBntjdiMdu4q0rCvnGQ7sJRmJsrG7nm28ZfJTM6e6k37ZSyruEEM8B5wF24L1SyhcSiw8kvj9pL3/uLNZ97/kB3//kqQMsK0rjPX/clPJ9ltPMy4daWF7soiy7t9Pz9WuK+cNLx5BARwg6QgGefWUjZ8+NP7Tu21zL7148hNtqZmWpBynjKdtAn873j+2o40N/3QLAzevK+Owlc7EO0ukux2XlutWpQ+vazAauWn7i0VH2NXg53OxjeUk6ecN0PO//q999bPgXTWO/Yvzy2x5OKYX90+t1/On11MnNLluUy9IiD3/feIxclxlvMMoH/7oZu8lIa3cEp8XIH2+uYklxBlf87Hl21npTmoDVdgT5+oM7eGh7A+UZVnbV+1JGsUqzGvjbLWckh8UF6PKFOP9Hz+GPxPjWmxfhsJqSfQkau4JsrG4nHI0xP9/NDWtLyR2kHb5jBIMbmDQwGcAXlvz06YNDrmczQCAGh5oDQOrkWqEY3L+1jvu3DpwUrgP49v/28NOn9uM0a9QnRq774gM7+eIDOwes/9D2Bh7a3jDg+x7hqOQT92wd9pgsGrx0UOCxGWnwRrh/S2+4ntvXhO/C1L40/dsF92R4ABoHacJ1sCleW9QZiA6YyK7Q2FuT1OYffhKymJ7aDqr64D4CievinleP8uUrUx+qv33+EP/cfJwCj5VnPzvwsXLZT56n1Rfme1cv5uy5qUNZ/vqZA/ziucOUZjp46GMDm1P95ZVqajuCvPOMkgHL+opK+L8nh75OAEJRSWWOnS01HXzg7Fk89ZlzAHjxYDN/fKmaunY/3kCElw+1ENN1blpXNuz+ppPj7X6+9MBOPDYjW796cfL7iD506/L923pL/YOjaPrWHYrSGYhgNRnQZeqFNlwic2N1G+3dYc6oyMQ1gW3S8/PzsZs0AhGda1cX8//6TMkdCE/NsNdyLFVqE8DhmdhREEdiqEzKdJ4iosM/thHL7vjfHv71xnHWzsrk59evGOdQTQ+dwWkyHvsUOntOalOzg9+6lMovPZLynQTu31KX8t7vzyCgKM1KezBKjsvCvz+0DnefUV3XV2axvjKL3zx/GF8omph+RCaHzA5EBn++tfpCbKxuo9BjZ3FRGsFIjJcOtmA1GThjVibaIAPmTLZxKWKUUu4DBqS2pZSja2s1jEt+OvQU11uPDxyitqca8JY/b+HxT5+d/P73Lw2cQugPe+Gric8/fnI/bd0hpPRj1ODNywupbQ/wpct7m8B9/J6tyQT93a9WM7/AzdVV41etHIzEeHRnA7qUtHSHuXHt0G10T/bVOpJ35CM7G3lhfzP+iN5bexQh2cSnzR/hE/ds42+3rGZnbfyU90/b3P1KvGS257z01RmM8b1H9nD3e3tr0j749zdoTvRz+cIDO3nfhllsqm6jyGNj49F2OvxhIjGdfY0+st3WQeOoOxzDPeDbVBEdBhlFeIDACIfsHoo3FEuOUjXRQjqgSxoGGRr8UIufXz17KOW7+V95dMwJgTO/+Qivfb132/c/cDz5ubZz+ONd+o0nUn73o4/29iXqHuSc/O31eGFFdWuAe16v5rrVZcllX/3PDvY2xK+9z9+3g1dvT830/N9TBwhF45Pd3vPaUa5b03u9bKlp56Ht8YyeLk9+iusdtV3UtHbjtJowGwVfS2TefvDYfpq9QfY2eGnqDBKVkkd3NXK8zUdRxsj7n0wlfyRGGtARiPLI9louXRKfs2H1d54ecps9Y/ytg01eukNR6joDeIPREY3mdKzNz4uJIe0lDFqzOV7e9dtXkoU3f3o19b1y5veem5LE9azbp76WB+CtvzyZ2b8m1mTU2IzVLX/ZQvUdo59c9PcvHSGqSx7Z2UBDp4+8tFPjeTIan/zHdt6yXDXf6+uqnw393B1OTMLRxGBe3mCU7zyyZ9A+r29eVsDeBi9zcl1kOi18+NxKth/r4PIl+QPWBXhmXzPH2vzsqfdSkmFnZ10n2xPp8yynhbl5UzTcax+nRLuKHbWd5AfHlmDc3+QbUTvnwdbZeLSDjYmO1c/se27Q7WJ6fDz5g01dvP3Xr9I2SId0l0XjqhVFvHakDavRwI+uXcat/9zG9uOduK1GlhZ7mJPr5POXzEPT4sPISinZeqyDdn+YJ3Y1cP78XG5aV0YoGuM/W+voCkS4bPHgF954k4A3PHxi8EirnzO+++yQy080tcZzB1pTzkHfUW+DEZ2fPzN46frxNj//3nyMXzx9gA5/hFnZdj53yXzOGaRpoxLX5h2+BmY01s4ee5+0/qPGnUjfS0h01BIfNDLObTEmrzHvICWCkT4jyhW4U9sie+wm2rvDBKMxVpWNz9DVncEYncEY/3z9KH98OXU4rZQ+LlLy9Qd3U9MeoNBj55VDzQSiEqMm+MDZ5Rxq6uZISzclGQ4C4RhRPUZRhp10uxlNCObnu5mT6+S5/c3UdgQozbBzpKWbzUc7sJsNRHWd6hY/douBT50/mxcOtiCE4POXzqN4kPbex9r8PLarAY/dzJVLC4ad72NW1omKFE6s/3O3b6LzwW29NY5rvvNUyrK+2/X9Xpc6920+hj+ic+Pa4hFnevru7+0L4HvvGkHiVwxdKj/xjesUpZfFpBENxTAIyBhjX4uXDraws7aTZcWeMc9XdsZ3nqTZF2ZlqYd/fGDdqLf3dYc5+/+eoysY4aIxTDw5k+m6znce2cvhZh/XVpWwo7aTlv5Nd8ZCDD3oS3xgrt6n2YbKLDZUDt0twWMzcYx4iyaLSUs2Q9eEwG2bHtmN6RGKU0zfpltOq4FgROf3L1bjDQxe7+IN6TyaGEffZNB4cGstO2s70aWkzR+hyRfAH45R2x6kONOOQRMsKIj3lTnY5EXXJU/tbeSmdWUcawtQ2x5PtO4cZBLGmWKklSISqO3w05IYAvtQs58tNR2sH+bGPN311GqMh//sDPCTMW67u2Psv3v70x1ce1Hv368c7h0AMjJIZ7a+32yt7eKs+b0FBkZNY11lJr5glLm541sS1T3II6F/x/6XD7VgMRvp9IcJJDJnUV3y3L5m2v0RdF2y5Vg7drMBKaHdHyHHZcVi0rCaDDR1BantCLC7rgtd13nxQCsxKTnW3o1RCLpDMSSS371UnRwV6bm9TbzzjLIBYdtR24k3GMUbjFLXEaCs37CxFdkOMjxWblxbwtyCqSm1G64Qa2N1OzFdYtJEstZ5tO7dDd8bwXovH/ANuezEM8DNbJXZNg42j1/hijK8Rz6+gZ8/c4hrVhZjNo8t07Opuh1dSjYdbR9TpmdzdRuN3ngifOuxsaVN/rm1lvZEE79n96nBMPo62upnR6LW5LFdQzeBH4rLFO9ykeuxYBQab1pWxOtHWllanD7ou2AszpuXw+xcJxkOM1aTgUWFaWQ4zJiNGlnO8Z1eZaxUpmcMNBH/ZzFqFCU6858xK5P/bKsjMkRqfU15JttqOzBrBs6fn8t/t9dzuKUbu0nDZTFTmukgv0/fnQUFbg40+SjNdBCKxlhZGi+BLvBY8djjEy32H052Juk7v81QBPF+O3NzXIQiXXSHY+S6LMzOdarJuIZxw5pSvvrb3r9PpknH5wdOiTMpbr00tSbvrSsK2ZQYqj13kKGY7WYD3Yl+eRfNT60hzXZZqMh20u6PMC//5Gsv+nKZoG8rQ7NBIIQg1Gdc58VFHvY0eNGMqX3Qalr9WM1GdCmZleUgEou3q85Ns5HtMiMQ5LgtzMtz88rhVnyhKO3dERYVutlV7yU/zUZ7dwiRaEf95qX5yThaPUSiZk6ui0NNPtw206B9Ce1mIy/edv5JxsrJeeljy1j/s62DLttQmcUfXjqCNxjlvHljq+2tGGHl5S1nlnDnC0MPqDAVqu+YHkNWqwzP5CrKcJ70kPzz813squtiwRifgYsLnLisRrzBKGWZox81DODSRdn84DEDwajOokI3w/eePL0UptspybRzrM3P2opMfjHMugvyndywpowbhukaAXDNquH7sI6WpglKM1MLygrGeYTik3VKZHpGOjnpl64s4VsPnvgldFGFmTtvPn9AB7ChXDA3i0A0xuIcM2abC12XXLEkj1lZLgxGLTlJ353Xr+B4u58cl4n3/3lryj5+dv0KwuEYBgMYDAae/Mw5+LrDOB1molEdY79mJEXpdj50dgXXrSrmUJOPuXnxB5HdHB+aV9fltOgU1pfHauDbb1nE2nIPManxl9ePEY1GcZo03rWhkj+9tJ9fP1fN5csK+Oi5lfzx5SMUpDsASYsvQkWWnbtfOkq600KG04IvGCEaDpCb5mBBcQaVOS5u+9c2uoJRitMsfOeaZURiOosLPAQiUf79Ri2zsp1smCbjyp8o4zbYRKcTzWUxcM681GYDK257mDfGmPFZvH7sE5KMduo5k9bb/2pRfurDvCTTQZ7bjNcf5tx5A89/tsNMdziASYNgLPWsWE0GzpqTTZsvPKBm42S4zIKvvHkRP3xsP21dIc6dm8FnLl1AodvGR/62mecPtlFV6uErb1rA7144QkzXee1IB13+IDEETquRxYUeVpWl894zZxGOxHhkRz3/3dmAy2rkw2dV4HHG57BZUuRhSZEHh8XALWdVEInE8Iaj/OHFaqTUyXFbede6cvTE4BGaphEIx9hd30Whx5bM4FTmOPnIuZWjfrb0ZJz7D38OqedtPDy8b+galrw0Gw9//CzC4RjmEQxkMpinvjqye+Fzly/mc5cv5tUjx1lbXoTl7k8ml12/avT9MsbDdMjwKKemixbmccH83DGnK8xmM9u+enEyXTMWeWlOdn3jkuQ+LF8b025mJLNR4/tXL02mFz85TBzvq/fx9f/s5MsP7CTXbeHBj22gKxilKzGnRJrNxKzsmdfvayROiUzPjtpORtJ75ZGtQ89t0tcTh8I0+kbWAMFjM/DCoTaklBxrt/LpC/NxWozMzktLmZF889F27kx0EL9gQQ7z8lzJjtU9+r+Eex4M/TM8PTRN8O83avGFouxp8KaM8jQdMjxLcmB7U+/fb19dwpnzcpOz+J49N4fDzT5Wl2fgtJn4/pPVSOBvG+sIRWK8Wt2JPxyvprWbDTR7Q0gpkfgoz7QR0ePHqRti2Jr97Kz10uQNE47p1CB4cEsdVrMRKQUvHGjhpYMtCAEZTjML8tOGGMV+8pyopmqyMzwAvlCMJl9qKeyYZwYGEGMfLmrgjEHD65tw3nikmTPn9Jbk72vsotkXQddTm7r1qE40CY3osKWmjWUlvX13Wnwh7t9Si5TQMZqJhk7AG5Z8/T878YXjcfTcoXbm7WikJMPOcwfbkMT7DT6zt4V2f4Rj7X4CkShRKZIlZhvmZLKhMgchBK9Wt/ON/+2h0x/hhQOCSFTnq1cuwmjQOH9+DvsavCxPHJfJZCDDZGD97CyOtwdYVxGv2ek7Cerjuxs43NyNySB474ZZ2BLPp5N5tgxWazhchqdvwUBV8chKh1t8J262NtYMDwyecRvO2vKBo3Het7GO77xt+ZjDoChTYTzSFWPN8Iz3PmaqnvRia3d4yHRxDJKDTtV3hXjLL17kbSuKOZ54DxZn2Ll2VfG0q4WZDKdEpmekNteMrA23BNbf8cwJ1/vqFfPZdqydB7bFE+Y1bQE++Y+tJ9wuGtNxWoxYDL19U8puexijgD/dvBpfJIYu4Zy52ZgNgh8+vp8dtZ28fVUxVyzpLSGUUiaHau0/ZOt00DfDA3DX80e46/kjI9r2vq29E/tpQIc/0qefg+RAsx8At9XAsZZunt3XjNWkEdV1NCEIRGI8d6CZlaXpfPZf26htDyAE2E1G2hIjxE2/GJt6EvjLK4dOuN5I+YaZpHMiBSOpZ3fnjr0kRtPkQNPQNQEAkX41PZFolCd3NdAViuKyjD2xPJieDA9AMCr5yVMHUpZL4K+vVlPfFUq9XmOSzdVtICUt3ghv1LSx/XgnXYEoEohFJRuPtHHnMwdp9YVo6ArQ7Avz3221NHlDZLustPjC+MNhOgJR7CYDCwvSKEq34gvpLCiI1xy/dqSVDn8Es8HAVSvy+fMrNfgjMdaUZeCPxDijIpOcPs0Fd9R2JmsT+mcMhhpcYDh9z8SmY/4RbTMcKSUvHoxnIs+anYWnzzCsfcMngCPjMFLXUDUr49C9WFFG7OIfPce+Rh9ZThObvnTRiTcYRM/0FQbg0BSOYvenV6o50OjjHavVSG39/fW1o+yt93LtqtHFTW1HKGVKDpfFwEsHmjGbNIIRnQvm5zAv383Lh1qJ6ZJgOMZ583M5c3YWz+5rRghwWYw0eYMYDRq6ZMDz9VQxozI9462uzcemxOhto7G5poML5uYM6IwflfDRf2zhxrVlAGQ4zOiJYSZD0Rh/eLGadRVZyc7GQgjeuqKIw80+5szg/jvD1Xh09Rm1LxDRsZs0jFq8K3goovP64TaOtvmJJoo1rEbJtuMd06aJ23T0+tHR1rEM7YN/2071ksl/Od314lFuv6J3Hp/7jw6zcj/feeQAt5zdO1fR1mNdNPnC6FLy0qHWYbacGHVdgyeRwzpsPNpOTVuAJm+QqJ6aka/vDPDQtjpMRsGBRh9mg4YvFMVi0tjX6MMEhBIbdBClw9+CzWzEYzdR0+ZnabGbZm8IXyjKc/sbae0OsfVYB5GYzp66LqrKMojGJG9bOfi8Yn1rRCazWdVdLzcOuex4e4BN1fEaf7NBcMmiwctCx6NARDUlU3pM9TDY+xrjBT0tvgi769tZkD/6USh77onJmVhhcIeafTycGGjnjy9XT2FIpp9jrX4eTMwFePdJxo03FGNHXSdSxvumd/jDVGQ7aPdHOdraTbbLQmNXCKtJY099F/5wfM40h9lIbUeA+fluTJrg0kkaQXg8qd7ew9h0qB5tDDFkAooybRgGqSmuzHGiCYEQ8Q7UhRk27ImmGLluS/Jzj2yXhTWzMkmfwdW9g8VTD1uf6NCA3DQrc/PcpNktuKwmVpdnYNTi8Wk2aqTZTczKOj3bqipjU5ppJ6ZLQlGdLOf0us9MBoHHYcJiMsQHUCFeSyEAl82Ey2rEpGlYjfHlEB/iXQhBn0omBGA1G3DbTJgMGg6LgQV5HvLcVmwmAy6ribJMO41dQZq8oeRQoznu6THizki5babkMNvZgwxoMdNN9TwzytTo+wotdU+vZ9hoZDnMOC3xsviSjLENhjBTeRzx5z1AUYbtpJrvC+KZHZNRQ9M0PDYzuW4bBiFwWuPviBy3hfw0K0LE+71mOc1YTFqydudUezf0OO1qei5ZkMOju+PtsjZUZvDiwdT2/2YgDJQ6BWa7m/PyXJRlOfjJE/toT9Q6nA1ceOUCvvrgbmLA1cvzmVUU4XsPtVDshhs3zOXcObl87cpFXPmz5/EGImS7LKwoTed9Z1ZiNAh0SbJG57c3VXGg0UdVWQZW0/g2r5koGRYIhCAAZBuheZCheXOdJpYXpfHo3vgwFG6gq8/yRQUuyjJsZLltzMq0sbO2i8d21eEwgtFs4arlxZw7P5ujLX52HetgeVk6S0vTCUck++o7MZsMnDsvlzcvL2RffRfz8t24rSbmTIMJsKaz6jsuT+l0fTK+fNmcE680Ab5/9aKUv69ZWcQ/N8cnRs0+Qcblj+9OHXzBZTVx8cIc2rrDnD03h5+Ob1BPaHamhQZfhGBIjzddI/5SKvBYef+Zs+L9dIRk+/EOitxW/vL6cUwGwfISD1etKKKtO4zVZOD+N47zzN4GOoOxeOLBZqKhM4jLbGBOrpPr15bjsZto7Q5T5LGT7jCxosyDNxAhy2ml2Rfi3LnZRGKSs+dms7jQM+gIbj1GmsAebsCOvqONjXR/w41QlmYzcdO6MvzhaEqzvBP91vevXsJn/7UdgI1fvGDU4eh/T12+YGqGzJ8utU/pVkPyfalMvKc/fQ5femA7Hz2vAodjbIOx/OCtC/jW//Zxx9sWj3PoRi7Nbub7Vy/leIefRQVuPjNlIZl+XFYTP7hmKTVt8bj58wgH+AJIt0Ca1US2x8rq8izWlnswmyzYzBodvgjzC93YzEbqOwI4zAaafSHm5LqwmY3cdIYFIcBiNNAZiOC0GvGHoinz95xKZlSmp//8FwC5TjPfedsSzp+fy/P7m9l8tJ1lpRncdEZZsuMuQIc/nOzMXJHj4MndTXQFI1SVZ3D54nzevb58wO+9c13qdx9eD5/8xxbufvko9248zt/ev4YHP3YWAAcavfx3ez1/fa2Ga6qKUjqQ5aXZyEub3A5lHzu3go9fMIen9jTwwb9sGbC8b6Lg9SNtbKxuY16ei/PnDz1h2D821vB/T+zHG4gQjumEYpLKgjSq372Ge16vob4zSLs/TK7bytryDOo6g9S0+Vle7OGuFw4TCMf4wLlz+fA5lSn7XVacwZuX9zax+eWzh3huXxMuq5ECj53V5ZmsLh/bZGoCcNuMuC1GmrwhQoPM8TKRVpd5eL2644TrCSA+CrcgeqKZXodgNwl2f/OyAd/ffenYa8auWZE65KVdgH+Ewbux389mWqB1hJ0h7CI1l31dVTEPbK0lpsvk8O593byulPu31pHjsrC2LCN1X2YDbd1RvMEYsTHGbX8aYDQIZmU72NfgSz6X0u0mBNAZiBCTYDEKVpRnU9cRiA8y4Q0m5tWBdZVZNPtCPLW3ifPm5fDOteXEdElHSKe2I8BZc3PIdlmTNRoryzJ4dl8zmpCsn52N1WSguqWbAo+NCxbksrAgDYgPfdqjJKM3geSwGinPdhKK6Cwt8gz6UltcmMamUdYmnGjAjpFkdjx9PodCIYxa/D6wD1JI5LQYk6XFI/2tJUUePnvxXAo81mRh1EgMtb+du0eaHBlf02XI6i9dMZ/P/GvnVAfjtFGe4+Cvt5xxUvu4enU5V68emM6ZbBlOMxnTrMZ9uvDYzcmalpEO8PXbG1dwwcKRNUPrmQaloM87om8ro54081DP11PBKRfyZ249hzufOcjLh1qo7UgdgW2w5EoopicT6q3d8RRVIByjOxxNyfR47GZu7pOxOXvO2OZ4qEuEKRCJ0dgVIscdz8y0JDrX61LS1h2e0FEzhIBrluVx75bBJ7DSRHwuE5NBY0ti3g6Iz3mz6xuXDFh/dXkGq8szBnzfX3cohsVoIGiIEdUlDouRY23x0UJau+PHn59m40PnVACwMrHd/kYvgcQcKnXtw8/vIKWkvjOABPyRGK2+EDCymp14xkFg0HrnSREC5uW5qe8MkO4w09YdxqgJCjw23r6qmL+/fpQjLb1huu3SedS0dnOkxc/q8gzWzMpgXcXQpbp/fvUoz+5twheKcrDJSyiqE4zEKMmwk+2y0uoLYTYIwn0yW2aDYGmxhxyXhaJ0O1aTgWuqiihKPIhm3fZwMiF5yYIcdtTF68+WFqdz5w0rUn5/Z20nT+yO94FYO8TcLL/dH+bss3v/vvP6Fdz9SjUA584deB/0JDiFgGMdQRb2mXQsM82KP3EPmAZpGuqxGfGHYwghuO7m1Bm7//nRs/lPos3y0uK0QcPaY9MxP5ev7P07221NZpYXFgycZ+KrVy7iq1cuGvA9QDCqMzdRO2gewfxOf37var79393sbRx8wIQMh5mbzihFl/G4spmaaPIGsRg1brt0PhctzOMPLx2hwx9BE4IPn1uByaAhpeTnTx9M3Dvx+bx62nD3PLsMmuAtywuHDNvZifN16eI85uWNbr4Np8XIu8Y4Sd2eb1zCr58/hJTxJrnjYagMRbM/TCwxsEsoOj61CXPzXMlrYDyMoovZuGruCiEESDl8s+GJdtbcPD5xfvyanZXt4JtPTl1YRmM6ZBgVZaxU89YTO+UyPWWZdq5cmo8udaqbu9nd4MVlMWI0CCIxSbOvdzQpowa/fVdvyuis2dmYDK3kp1knbHbYT50/m189f5gFBW4WF3mS3y8v8eANRjAZNOaN48u1571mFHDj2lLu3XScilwn37pqGa/XPEd9RxBXTKZUgy4qcPPOxPDXX7hsAY/uaqDFG+YzF51cU6ULF+TS5A1yrNVHVyID9InzZwNw8cI8dtd3sWiQBOmcXBdvWpLP4ZZurl89/GRZQghuWFPC/VtqKfTYkkP0DsYget/6drOBbKeZ4nQbmqZxqNlHMBJjaVEaxZlO3rG6hJcPtVDT5scgBIsK03jX2lIcFiPf+u9ughGd8+Zmc/HCPLYda2dhYRpZTgsrhvl9gPPn5YAEbzDCFUvy+MfG47htJubkupif76Iy28mn/7mVQChGky+MUYPrVhZQmuNmTo6LrmCUDIc5meEBePuqQv61qZZMl4Vf3LCCLz2wiyZviE9fMHvA78/Lc9HsDRGJ6Swv8Qwaxj+//8KUvy9YkMvBZh/doShvrxo4SMHVKwp5aFs98wtcLCxKzZzc98G1rP/ec8Sk5PbL5g3Y9iPnVvKHl6pZWuxhYWHqtuVZDlaVZeANRgatuVtS6GJ7rRezAb76ltQmGMUZNtaUZ9ARiLC2YnS1foUeG2tnZdLWHWZdRRamYYZtvWJ+LmfOzuaOqxbyrj9uShloAyDHZeb9Z5Zz3vw8dtV1sbDAzewcJ//eWsuCPHcybBfMz2XLsQ5m5/ROpCuE4JJFeexp8LKkMI3STPuw8dHfytJ0ukMxbGYDs3Mmt4mnzWzggvm5HG7pZlVZ6j1x5Sx48PD4/VZRuos1ZRnsON7JxYuGrnmeSlOV+Mh2WzijPJNtxzq4cOHUxU22y8KG2Vk0dgWHLRRSUqlE69BU3KSqzHakTPvwo7cNXqinpBJyEoZCFkLkAL8DsoDngB8BfwLSgW9LKf8z3PZZWVmyrKwMfziGNxghEpMEIzE0EZ+RvG9VWzCi09AZIBKTuG1GspwWNCFo94eJ6ZK0Ph1de3iDEQIRHYfZgOMUq7Y7cqQaV3Y+QsRrMJq6gugSPDYTuW4rwUgMbyiKxaglOycDRHVJhz+MIN5Rum8GYTz5QlH84Viis7SRSEynIxDBIAQeu5mJnG6ourqagqIS2vxhwlEdu9mAEAJdl9jNBvyRWCIcJrQ+xx+O6nQGIhi1eBgnKGpOyBeK0uGPzxvjsZtOWKXcE7eCeEmvEPEaTOMgkbzv4GHMnlzsZgO549g2NxzVqWnzo0tJfpoVl9WUsjwQHvx6BIjpkrqOADFdkuO2YDenHm8gEsMbHHzb8VRdXU1JaSlHW/10h6IIITAZBAJBYbptwGAjp5Pq6mrKysoGXdYZiBCK6rgsxpRa9EhM52hr/JrIc1tx9zl3gXCMRm8QTQgK0mwYR1g90RWMEIzoOCwGHOaTf2Z3BSK0docxGzUK0mxjuuf3HDiE5srBatIGzEo+mfY2eInGdCxGLdlcZar1vW50KanrCBKN6WS5et/f+xu9hKM6xmEKBk/ld/VQhrunTnfb9hxAc+cgiBfgGUdQE3+6OHj4CCFLBjqgCUGu2zJhhfmnms2bN0sp5aAXy2RlekoBn5SyVQjxNPHa/z8Bm4AnpZRrhtu+qqpKbtq0id+/eIQjLd1sqm6jMxDGYTFx1pxsvnzFguS6v3nhEPe/UUtnIML8PDcfOKeCDIc52Uxkfr6bSxblJdeP6ZKfJubOsJsNfODsinE++ok1d9EyPvijewHYWdvBlpoOIjHJnFwn379mKS8dbEk2rXvfmeXJROjLB1t47Uh8EIdz5+WwrNgzIeG789mDhCLxuXU+fn4lz+xrYtuxTgAuW5w/rk1K+quqquJrv3+Qp/Y0UdcRoDjDhlHTkpnBnkEj+jcF+t+OevYlJpa9ankhZVlTk4D52VMHeOlQCwLBhtlZfOTcymHXf2ZvE1uPddDQFcSoCbKcliGb32WVz+f8234PwB9uXjUggzFWv33hMH94KT5XU1VZBj+5LnWCxj+9Uk1r4nq85axZKQmXp/Y0ctfzh5PbfvbiuSnb/vW1ozQlhnd+z4byCcv4VFVV8fsHnuSG37xKdziGlPGmn2l2E5csyuPLVyyckN89FVRVVbFp06YB33eHoslzl+k0pzST++NLR/jNC/Fly4rT+UWfJpg/eWo/Lx+MDxN+49pSrljaO0/ZUCIxnZ8n5pxwWY2878xZYz6eHp/71zaOtsbnCfr6lQuZlz+6poEAaSXzWPThXwDw1/etoWwKRpFs84Wp+vYTQLwp5IFvD+zDNxX6XjevHmrlR0/uB+JNUb/ypvj9VHn7/9ClRAjBti9eMGCCyr7vaofFwC1nnVrv6qEMdU8pYMmfTf5NPwbgI+fM4rOXzJ/aAE0jubMWYHv794F4i59z5mbx25tWY5gGE9dPNSHEZill1WDLJiXbLKU8KqXsmQBDB6qA56SUXqBTCDGiVOWiwjTS7Sbm57tIs5tJs5k4a3Zqgm59ZRaZTgsOs5HZuS5mZTko9NjIclkwGzXm56cmsg2aYGGBG4MmWFw4fD+C6chq0rCZDXjsJi5bnE+a3YTNbGBpsYcCj41FhWkYNEFFjjOlpqAy14k9MYRt+QSWSi5O/P6iQjdCCObkurCYtESTrYkfvGF+vps8t5V0u5mKLCdz81yYDIIz58Q7emc4zBSn2wdsYzbGh2wcbvSqibaoKI38NBt5aVYWjeDanJPnwmoyUJ7poCjdjsNioDJn8ISX02LEoAmWFXvGLcMDcMH8nERtqoFLB5kjZWFB/HqozHEOqDFZXpxOduI+7X9fAyxKbDsr24Frgkt5SzMdzM93oQmBzWwg02nCZjJy+eITJ8pPR3Zz/FqLP09Tr9Xz5+fgsZswGTUuXZza5Oqs2b334aoR9BsEMBniz/H4c2V8ntlnzc7GqAnKshzMyh5bZsVpMaIJQWWOi+JJeLYNJsNpJttpQQhB6TQd8ndhoZv8NFv8Pp/TO59aSYYdIQTZTvOADA+kvqvH67wr01tP01+DgPcmmuQrcR67CYsxnsGxmw2cUZGlMjwjMCk1PckfE+JjwHxgmZRyXeK7fwEfl1LW9Vv3FuAWgJKSkpVHj05V19DpTZUSDU3FzdBU3AxNxc3QVNwMTcXN0FTcDK0nboYaROF07suirpuhqbgZ2pTX9CQC8V2gFPgooAuRbDXtBtr6ry+lvEtKWSWlrMrOzu6/WFEURVEURVEUZUQmJdMjhDgLMEkpb5VS6sBuYJ0QwgUgpQwOuwNFURRFURRFUZQxmqzhT9YBlwgheqqb/gz8P+JT69w2SWFQFEVRFEVRFOU0NCmZHinlHcAd/b7+3WT8tqIoiqIoiqIopzc16LmiKIqiKIqiKDOayvQoiqIoiqIoijKjqUyPoiiKoiiKoigzmsr0KIqiKIqiKIoyo6lMj6IoiqIoiqIoM5rK9CiKoiiKoiiKMqOpTI+iKIqiKIqiKDOayvQoiqIoiqIoijKjzYhMTySms/loOwebvAOW/W97PfduOsaeui42H20nEtNHvf+XD7bwl1eq6fSHxyO4E6ZvOKWU7KztZMfxTqSUyXX21Hex7VgHui6H3E8wHOXvr9fw5O6GyQj2pOiJj4e31/HHl45wpNmXXFbb7ueb/93NQ1trpzCEkysYifGNh3bx4oHmQZfvqe9i67EOYsNcJ2PhC0V5/Ugb9Z2Bcd0vwMEm35jv8b5iUvL6kTae2NXA1x/cya+fO0iLLzTsNnsbJia+Zorb/72dm37/Oi1ef8r3MV2ypaadfQ0Dn92nmu5QlOt+/Qq/ef7QlIajyRvk9SNtdPojUxqO0bp/8zEu+8nz/Pa5qY0/ZfroDkW5/KfP8+3/7prqoEw7UsJH/7aZa375EvsauqY6OKcM41QHYDy8cqiVzUfbAbh2lZECjw2A5/Y1cfcr1YSiMdLtZpYUeegORTlrTvaI9320tZufPX0QXUqOtfv5wmULJuQYTlb/cF65rJAndjcCIAQsKkzjYJOPR3fGMzJRXWdlacag+7r7laM8vbcJALfNxOryzMk5iAm0q66Lx3bW89D2epwWI68daeOX71wJwO337+Bgk4/HdzcwJ9fF3Hz3FId24h1vD/DYrgae3tfE4x8/C7PZkFx2uLn3OonEdFaVDX6djMUjO+o53h7AZBC8/6xZWIyGE280Ag2dQR7aVgeANxjhnLk5Y95Xpz/CQ9tqeWZfM12BCBajRrM3zJeuGPzeP9zs45EdExNfM8GPn9zLvzYfB+D9f3qD+z+yIblsU3UbLx9qBcBi1CjLckxJGMdDTZufWE07W491cNGCPEqn4Fh0XXLf5lqCkRj7Grq48YyySQ/DWN12/04iMZ07HtvHu84oxmw2T3WQlCl2tNVPqN7L3gYfZ8/NYcPskafdZrq6zgCP7GxESsn7/7SJZ249F4MmpjpY096MqOkRYvDPWuICEIjk9+IkrglNnBrRpQkNwcADTT32oSOi732jnUyETSP9D0NLuWZ6/zAYZsbxjtTg14nos3ycfy+xbzHO15UQved4vK5ZQe/xa8O8TCYyvmYCwzDPzZS4m0GRZ5zCxEfvu24GRahy2jOdZu/m0dDEYG9yZTAzoqbnjFmZuKwm3FYj+Wm25Pdnzs4mEI7RHYqxsNCFP6yzqGB0pfilmQ4+ecFsDrd0c8WS/PEO+rgpzXTwqQtnc6g5Hk6nxYgQoEvJgkTNRUW2k8uX5BOO6snvBnPTunLS7WYynBaqZkip9YJ8NwLBooI0mrvDnN2nxOiOty7mjy9Xs6woncoc1xSGcvIUpdu4fEkB587JTqnlASjPcnD5knxCEZ2Fo7xfTuTSRXnsbeiiKN0+brU8ALluK29eVkhnIDLqe7w/j93EW5YXcv68HF4+1EpempW3LC8acv3yLAdXLMknOAHxNRN87Pw5NHvD1HUG+OE1y1KWVZWmYzVp2M0GSjNP3VoegNJMOyvKM7lwQQ6FGfYpCYOmCa5eWcTR1u5T7ln2/bct4bcvHuHtKwtVLY8CQFmWnTmFaayvyGTNrKypDs60UpBm45yl+dR1BfnWmxcPWzCn9JoRmR6jQWNZsWfQZRctzDvp/a+ZlcmaWdO/idfq8syUpmjzB8nYzMk98YvQbNS4uqp4XMM21YQQLChws2CQRGlemo3bLp0/BaGaOlaTgdsvG/qYR3KdjIXDYhyyWeXJKh+n5kSaEMkwnjMvd0TbzJ6g+JopvvGWRYN+r2mCJUWeyQ3MBLGbjfzlfWumOhhkOS1kOS1THYxRu3J5IVcuL5zqYCjTiN1s5IGPbjjxiqchIeCH1y6f6mCcckbdXksI8WYhxCcSn11CiFO7eE5RFEVRFEVRlBltVJkeIcRdwLXAJxNfVQD/HucwKYqiKIqiKIqijJvRNm87W0o5VwixB0BKuVUIMWsCwqUoiqIoiqIoijIuRtu8rU0IUQhIACHEWiA27qFSFEVRFEVRFEUZJ6Ot6fkQcB9QIoTYCBQC7xr3UCmKoiiKoiiKooyTUWV6Es3Z1gPzEtvuk1IGJyRkiqIoiqIoiqIo42BUmR4hhAv4FFAF+IEnhBB/lFKqJm6KoiiKoiiKokxLo23e9negEfgxYAPeRzwD9KHxDZaiKIqiKIqiKMr4GG2mZ5GU8oqeP4QQjwAHxzdIiqIoiqIoiqIo42e0o7e9IIRY3OfvEmDbOIZHURRFURRFURRlXI22picfeFUIsQWIACuAXUKIx3tWkFJeNI7hUxRFURRFURRFOSmjren5JnAZ8EXga8CVwBeAb/f5NyghxCohxBtCCKsQIlcI8ZgQ4nUhxJvHEvBIJILfH0n5TkqJrutIKZFS0tHRQWtr67D78fl8KdsPJxrVxxJUotHomLYbDSkl4XCYcDic/LvneCKRCFJK/H7/kNvqeuqxdXkDKdv36H8s/bcbDV3Xh93+ROdjpHr2M9j+eo69bzhisVhK/A0WnsGWSSmJxWLJfcVisUHX6bvvvp+j0Si6PvD7/vsIh8PJ3+hZL77t8Oei//Kea2UojY2NQy470bkZLiyhUGjYbYcLV/84HY3R3IdSSiKRCN3d3clnSUdHR8px95xrXdcHnJP+xz/UuZwMQ11LY9XR0THksmBw6ME8u7u7xy0MAF6vd1z3Fwye/HN6vI9xrCbr2jqRB3bsHXJZc3PzgO9GE+6e+7NvnI/kfdTzG9PlXCnD27x581QHYVrpf4+Ew2F8Ph/BYDCZFgiFwni9PsLhcPL53zdtPJXvo6k22pqeTwN/Ax6UUgZGupEQohT4DL2ZrM8CdwCbgCeB/4x0X4FwjOt/+wpbazqRQLrNyBOfPpPukM6n7t1GkzdERbaTY60+DrfGg2gzwZ5vXj5gX5f/5Hl21cdfnG9ZlktZlpulRR7OnZeTsl4wHOXLD+7ieFuAa1cV85blhSMNLr974TA/e/ogFpPGn9+zmjl57hFvOxISuHfTMR7aWsvLh9vQBFy5JJ+YFOgyxlN7m/GHYvRczhkOE298ubcybk99F7994TDbjnVQmePi0oXZfPLeHUggx2kmL83GvkYvaTYjTouRFl+Yc+bm8NN3LOcXzxzkhQPNLCv28LmL56FpYsThPtbq5+v/3UUkJvnMRXNYUuRJLtN1yX+21XK01c+Zs7NZWZo+5vh5bn8zW2raicYkRoNgSVEa583LJaZL/rX5GPe/Ucuehi4cZiOfvXgOd79cQ22HH5fVyLx8N7dfNp+idDs7azt5ak8TWS4zkZjEG4hw6eJ8KnOctHWH+eemYzyys54jLd2k282UZto51NzNkqI07rxhJc/ubeKvr9cAkgKPjWynBSFgb4OXbKeFLcc62N/gxWEx8N4N5UjicfnWFYXkuKwA7Djeztt//SrBiI7VqLGkyENBmpUn9zbiD8fw2E18/PzZ3LSuPCUO2rtDfOXBXbT5IrxnQxnnz89lf6OX+V97Eo/NxOYvX5iy/lU/f54tx+P3hd0Iu7+Veu+8eriVVw+3UpHt5Iol+QjRe94bu4J85YGd+CMxPnZeJavLM1O2Pe/7z3CkzY/VqLH1S+dhsViSy1473MK7fr+RmC65tqqIb791Scq2339sL//ZWkdxuo27370as9kw4uvgC/dt4+EdDWQ6zDz0sTNxWod+9PmCUc7/4XMcbhmYKMpxmvn+25fx6+cOsaWmA/QYwVj8PrSbNdZXZJPrNnO0LcCGyixuXl/OU3sa2dfoZVVZBusrswB4+VALrx9pY3aOi8uX5I/4OEarwx/mX5uPE4lJ3rqikFy39aT2t6O2k2V3vATA1tvW4/F4gHhmZ/E3nyYSkxS4Lbx8+wXJbf7w4iG+/t944ndRgYv/fvyskwoDQNltDwNgEHDouwOf7aP1lf/s4IEtdWQ4zDzw4TPwOEYfT7vqOln4zWfjn798Dg6H46TDNRaP7qxnb4OXqtIMNszOmpIwfOHvm/j7tnihySc5RN9QfP/hHfzihRoADMChO+Ln7/n9zbxR0878fDcXL8wbct9HW7p58y9epCPQm0m9cmk+CwvSeGZfE4sL0/ji5QsG3faxXQ08uq2e+7fXAZDntvBqn2tVmV521Hbytn82wD8f5oPr87jtTSunOkhTJqZL/v3Gceo6gpwzNxtdSq76xYtsOdaZXEcA/bMu2U4z5VkOorrEbjJgMRmozHFyTVUxlTlOOgMR/rnpGOGYzlXLC8lPs03qcU220db0/Aw4C9gmhPirEOIKIcQJM05SyqNSyuuAjsRXq4DnpJReoFMIMeK3Q21HgINN3ckT6w1GeXx3EztqO2nsChIIxzjU3JvhAQhEwBcYWHq8p6G3pPCxXc1ICbvruwasV9MeoKbVjy4lLx1qGWlQAXhkZwMxXccfivLgtrpRbTsSMV1S2x5gd70XXZfEYpIXD7bQ5A2yv8GXkuEBaOtOrR3b29BFdYufQCRGY1eAO589nFy/2RfmQJMXKSVtvjB1HfHan43VbcR0yeajbUgJO2u78IZGV0r6enUr3mCUYCTGK4dSa+N84SjVLX6kjGfKTsbuui6khM1H25FSsqs2vr/OQISDTT6OtfsJRmL4w1H+tbmWZl+QQCRGiy9Mqy/MjuPxB8ru+i50KTnQ6KW23U9Ul+xLXD9HWrpp7w5T0+onHNVp7w6z/XgnUkp21nbiC0Z5o6aDrkCE420BjrcFaOgMcripm6auEC2+EPsbvER1SYc/whtHO2j1heLXclNvwvs/W+sIRnQkEIjq1LT72VnfhT8cIybj98Jz+weWnu5p8NLUFSKq67x8MB7X4Wi81KfdH+Z4my9l/Z4MD4B/kNPaE6cHm3yE+tV+vlHTTkcgQjiq8/KhgbWsNe0BpIRAROeFQ+0py/78ag2RmI4uJU/tbRqw7YsHWpBSUtPm52Crb8Dy4bx0qBUpJS2+EJuq24Zdtzscpck7eI1Fky/MxiNt7G3oIqbrBGK9L5lAWKe2w8+eeh/hqM7+Ri8NXQH2NniRMp4o7tETh/sbvYTHWIM8Ekdb/cn77FDT6OLsRO7Z0lsT+OS+ViKxeEw0eFNr8n77wpHk5931J1878+r++uTn2DgVTr6wP35ttfpCvNLvuhwpvU9Y7t3SMD4BG6WYLtlT7028yzpPvMEE6cnwDOa3r9QkP/ett91d35V85uv60Cf2wW21+MOpNb5P7G7ktSOtSAnbj3cSDA98cOm6ZE99F68f7X2HN3QNX+usTB+/emlq7qnpoisQ4Xh7AF3Gr+NITLK3IfV5Othd0+oL09AVpLU7TG1HgGZviGZvKJl+OdYWf0eEIjoHx/kdMR2NKtMjpXxSSvkRKeUc4hmgdwD1Qoi7hBCrR7Erk5Sy503fBaT1X0EIcYsQYpMQYlPfavDiDBvLitIwiHiuNstp5vLFhVSVZVKe5cBjM7G0yMOyot4aFY9Nw2kzDwjE2lm9NQjvWFWM1WRgeYlnwHqzMh3My3djNxu4cH7uKA4Trl1VhNVkwGM3c+2qolFtOxJGTVCZ42R1eTpGg8Bk1Lh8ST6lmXaWF3vw2I0pJ7nQk1qCuaTIw4ICFx67mVlZTm6/bH5y/dIMKytL0jFogsJ0K7NznJgMGufPy8GgCc6ek43NZGBteQbuYUrOB7NhdjY5bgvpdtOAmjWXxcj8fBc2s4FlxZ7RR0ofK0o8WE0Gzp6TjdVkTNYaeWwmFhelMSfXhctqIt1h5j0byijNdJBmM1GYbqM43Z5cf1mxB7vZwNLidCpznDgtRhYXxi/byhwnBR4b8/PdOMxG8j021ldkYTYaWDsrE6fVyBkVGeS6rczNdTEnz8msbCcLC9Moy7RTlG5nZakHq9FArtvKhjnZFKXbSbebmJvnSh7L9atLcVoMCMBlMTA/z82a8gw8NhMmTZDpNHP54oG1BksK0yjPcuC0GLlgQTyurSYDmhDke6wUZThT1j9/Tkbyc4ZtYG3KitJ0rCYDiwvTsJpSl68py6TAY8NtNXLBIPfK/Hw3moA0q5EzK1Jr8D58zixsJgNGTfC2QWpTL16Ui8VoYH6+m7k5zgHLh3PJwjxMBo3iDDtryzKGXddlNVKW6WCwesvKLDtnzs5idVkGZoOG2yIwavFnUZrNyJxcF6vL03HbjCwrTqcgzcayxDW4oqT3eHvicGlxGmbjaMueRm5WtoMslwVPv2tpPHzw3LnJzxfMzcRu0hDArKzUc/P5S3rXO6N8+LgfibVz8pPnxmwYee3ycC5fko/JqFGYbufcOdlj2ochUeOpCbh5Q8W4hGvUYdAEywe53ibbVy4a+vi/fWVvLYylz2tjRUl64h2cPmyrgWuqivDYTCnvteuqijhvbg5Wk4F1lZlYzQPfR5omWFGSzkULet83FZn2kR2QMuW+9Jaha/9OB2k2E7NznfF0UYkHs0FjVb9WMAYGJuqLPFYqspwUeWzMznFRkmmjJNOeTL+UZznIdllIs5mYN84tkaYjMdp2fEKIFcT78lxBPMPyD6AN+ATwjJTyy8Ns+yxwCfEmbWdKKWViEIQrpZRDNgavqqqSmzZtGlU4TxdVVVWouBmcipuhqbgZmoqboam4GZqKm6GpuBlaT9z0NNnsr/qOk2++eapS183QVNwMTQixWUpZNdiyURXPCyGOAXXAPcQzKnV9lt2fWDZkpqeP3cA6IcR2gOEyPIqiKIqiKIqiKCdjtAMZfE1K+bu+XwghzpFSPiuljAohlgy1YT/fBf5MvAnibaMMg6IoiqIoiqIoyoiNNtPzeSCZ6RFCpAF/BQoBpJTD9jSTUp6T+HgE2DDK31YURVEURVEURRm1EfWgFUL8nxAiAlQIIcKJfxHizdnum9AQKoqiKIqiKIqinIQRZXqklJ+WUpqATVJKc+KfSUrpkFJ+fILDqCiKoiiKoiiKMmajHbJ6zUQFRFEURVEURVEUZSKcsE9PohmbpHey154B9JPfSSkHToKjKIqiKIqiKIoyDZww05No1qYoiqIoiqIoinJKGklNT6WU8mDi87rB1pFSvjzeAVMURVEURVEURRkPIxmy+tPAhxOfv028WVuPniZv541zuBRFURRFURRFUcbFCQcykFJ+uM+fVwKbgTTACTwPXDYxQVMURVEURVEURTl5oxq9Dfh1Ypt3ADcCDuAn4x0oRVEURVEURVGU8TKS5m19rZZSVvb5+1YhxL7xDNBg2ts7Kbvt4ZTvNEAfYv35uQ72NHYn/15SmMZ587P45AXzkt+FojHeONrBO37zavK7+z60DpD86LE9vHi4g0y7kc1fuTi5vLqlm+PtAUozrNzx6D7sFiPfefMCdjV0E4nprChJx6AJxioYifFGTTuZDgtz81wj2mZn7cC4GcysTBs17QGuXJKH2WiiqjyddRVZHG7uptBj457Xj/LcgWbetqKI959Vwe9eOMxTexuRuuT6taUsLEjjpt+/Tos3QCA6+G+YNLCZNJxWE6GojkmDBm+EDJuBa1eXoWnwkXMrsRgNVNz+v+R2Vy7N5y1LC7hn03FyXBa+ddViDjf72Ha8g1gkyq3/3gXEL9aDd1zOCwea2V3XxRVL8mnoCiKEwKxpfOk/O/AFo/z0HctYUODheLs/GTcfP6+S379wCF8k3jrTIGBWhpljHRGiUhJNXEwCMGiQ6TDhD0bwRWBZsQu3xcTOOi+ryzNYWOjGZTURjcV4cFsDS4vScFk0fvFcNQBLC12YTUbOKE+n0RdmU3U7h5p7r0eAG9cU8+fXjg2Iw/Xlbo51RPCGIoRCUfyx1OVOM1gM0BqIx3e+y0hNZ/yEZDtMdAYihPvcGAbAbtHIclr4ypsWcu68XAB29Lluqu+4fEA4epZdtSSdH12f2pWv6huP0uKPoQGHh9n2m1fM5cYNlYMuO9HvXrM0l++/oypl2f97eBd3vVhNeaadJ289d8C25bc9jAQ+e8EsPnLB/AHLR2rHCe6pSxfmYjBo/Hd7/aDL73n/Glq7w/iCUc6dl0OO20o0pvNGTQdWk8aSIs+AbXRdsuVYBwDLiz1oJ/Ec6UtKydZjHUR1edLPpxPpiTMTcKDfuT3ReR9uf4Ntc80vX2LrsU7etqKAO65eNqL9dfojfPivmzEbNe5653JMpt4xepq6gty/pZa5eS7OmZszov31d6J7arKMJa4nOhxZ/Zat/c6TtHeH+el1y7h4cUHKss/+cxubj7bzxcvmcf6CPAAu//Gz7GroRgCfv2QeF8zPwWqUnPPDF0DCD69ZxI7jXv67o5GLFuTwjasWA3Dp9x5mT1t8v7OyHBxp6UYCHqvG1q9dCsCC2x7Gn/jt6jsuTwn3k5/ZwDW/fBWrycArX7ggJZy6rvPPzce57b4dyTb//eM7EI7xg8f3YjMb+dT5lRgMhlHF4eluutxT09VH/rqJh3c0DruOy6zx1pWFLMj3MCfPxetH2piV5cBiMtDpj9DqD5NpN5PttrCiJB2zcfC6kEPNPho6gywr9uCwnDjrUN8Z4FBTN/PyXWQ5LWM6vokw2kxPUAiRK6VsBBBCjO3tMErH/ZDf77uhMjxASoYHYG9DF/sbvVyzooTCDDsArx5u46bfv56y3vP7mwF48XAHAK3+KPdvPMxVq2bhD0d5cFsdMV3y/ceaONzsA0ATkOe2AfEEc1VZxpiOsef3d9V1AZBuN5Hjtp5wG3nCNeIOtwYA+PfWBnJdJl482My+Bi92s5F7W3w8srOBmC754RP7qciy8fuXqqnrDKAB+5u6cVuNHGsPDPsbER0iIZ2uUCjl+7ZAjD+/dIg0pxWL0cC/NlanLH9mbxPP7WsmqutoQpDrthCOSTZVt7Gnviu5XhSo7whw5zOH0KXkjZp25uW56QpEeO1wC7sbfAjgfX/azMu3nU+7P5K8bn769MGU34xJONAaHnAMEojq0OiNJL/bcsyb/PzIrkbeqOnAZNRo9YWIxHR21XUR1XvPxLZaLyZNsPVYOwII98u4AINmeABeOtI16Pc9fGHwJT5HdJIZHoDm7siA9WOAN6TjDQW49Z/beeLTZ5PhSB1hvuy2h1NeKH1f+vdvb+dH16fusyWRE9OB7z28g89dvji5bFafbb/8330DMj19bfjOw7x4++C/+89tjXz/Hanr//KFagAOtvj5w4uHuHlDRXLZkq88krwXvv/k4ZPK9JzII7uGf8lc95vXWF+RSVSX1HcF+eQFc9h0tJ1XDrUCYDcbqcxxpmyzs64z+fwxGzQWF6WNS1j3NXp5dl98v5qAlaVjfz4N54ofPZn83P8qHEmhzIn0vUaPNHWz8WgHAPdsqh1xpucjf3uDjdXxFPDn7tvJj65bnlz2i2cPsbe+i2f2NVGR5aQ40z5u4Z1Ma8YhrsfDcOf85j+8RkNX/B3xob9t4fB3ezM9z+1r5N9bapFS8sl7t7Lja5cAsKsh/k6XwJ1P7WV7bQdP72lKFlZ96t6dyMTyv7xew60XzsHttCQzPACHW3rTBR3B3hSEv3eVAeG++s5X6QhEgSiX//R5Hv74Wcll/9vRwL/fqE15B/c/7//3+D4e3dkAQIbdzHs2lA8ZL8rwpuqemq784egJMzwA3rDOn189xrIiL8FoDJfVxD3eIMuKPOxp8GIxCGIS1s7KJBzVOWtO9oB9dPojPLStDimhrTvMm5YWDPJLvaSU3L+lllBE50CTl5vXT5/rfrTN274OvCyE+IEQ4nvAK8QHN5j2hABLn8G3TQZB+gi2y/Y4ANCESJaS2s29pTUuc2++cagc8kj1bK8JgdFwcvsajgA0TWA1GRK/a0AT8WMzCIHdbECI+HpCgNEgsJlProTKAAgEdrNGziC5/vjxCoQQpNmMGDWBJgSmfvFgMRowGuJhtSXCr2kCi9FI4hAwT2DcQbwmSBPxuAKSv9ufQCTjdeCyyWc2iGTcjZf8xP3RYzS3QLZz7KPhZ/e7hmyW6VWCatRE/JljjIer73VsGSSS+i43GcfvHKXsdwLvi9xJLMlzjHFWOGef0sk0e+q1Z02cE4MQmE0T+/yYSMtPvMqUy+wT9/0fR06LKflsNGqDnwezBmaDIeVZZjDEn8k9LGN/tKSwmHqfK+5+O7WPoLS773vTZR1tGbOiDE0TI39OafSkkxLvI01LpLHi3xs0gaYNTG/1MBhEMr0zkveIECKZDpvI985YjOoulFL+UwixFbgwse2VUspdExGwvhYXptGS+Ow0CQJRicMUL/XuX+OjAW9bnsc/tzQkvztvXi6XL84jy9Vbere2PJO/feJMLv3JC8nv4rlXSTQa5VfPH2F5kZsNs+PNgawmA2+vKqaxK8gtZ5bxi2cP4bSY+PC5lRxpiTdvm92v9Ha0NlRmke2ykG43DyiNH4p5hInYC+dlsbG6g09dUEFXSFJVms6CgjRq2vwUeKysLPHwxJ4mblhdwtrKXL5yheTZ/U1EY5LrVhVTnuXgI3/dzLG2bo51DKwhAfBYjeSnmXHZzPiCUXKdZp4/1MbSIhdXV5UhhOTaqmLed2ZFskStKA3eurKcK5fkc8/GYxSm23jXulk0dQWpKstAE/De372GX4cil0aG08wXLpvH/gYf58/PoTHRvO3d68r48RP7aPGF+dHblwFQlm4nRPwl+a2rlvCb5w5wqCWAEbBaDJw1K43Nx7uJSB1fd4SYiF9faAbm5Tpp9YU53hXkzUsLcFiMvHiwhcsW5lKS5cJtNaIhufeNWtaWZZBmN/D5+3ag63DV8nwiuuD8+TkcbfWzpaaNJ/a0JOPJCnz28nl8/397Ccp4c6AIYNHgHVX5HGoJ0xYIE47FqGsPENUloURtUXmGhRyHkU3HuslwmlhZ6uDRXR0YgXkFTtp9IWq7IhgBowHsZo1Ml5V8t40vvWkBbuvA1ED/0rO+TTz+dXXugPXPnZ3BMwfayLIbuHH9rJRl+79zOZVfeJiohFc/uXLAtjl2aEoUrd7/8YuG/N273j2v/6b89sYVfOre7ayryOCKZUUpy1774kUs/PL/6I5I/vKu1QO2HSsBuEzQlai+KPJYuXZVMVFd5ydPHRp0m2c+czZN3hD+cJSViZrfFSUenBYjVpNGccbAWoT5+e7ky6F/LdDJqMh28pblhePyfBrO795/ZvLcVWSmvlb6Nxkai77XaI7Hwc1nlPDA1npuvXj2iPfxs+uW8NWH9mA3G/nSFQtSln3svNk8uaeROblOckdQwz6a8E6mX41DXI+H4c75D65dwdG2lzjS2s0/b1mfsmxlWQa3XTyXp/c38/U3LUx+/4ENJfz6xRo8Vo0vXrmE9ZWZ/Pi6ZSz/+mMg4S+3rGVXbSd/frWGa6sKsVjimfDfnG/m/U/F31frKjLZXdtGR1CyutSd3Pdlc438b1900HA/+6n1vPlXr+Oxmfj7B85ICet583IwCDB423i5pfe4+/rE+ZVkOMw4LEauqSoeTRQq/ahanlRWk8ZHL57NHY8dGHa9ubl2blhdSkmmk4ocB5uPdjAry4EQgmAkSps/QmbiGp2bO3i3CqfFyLWrimnyhkbc9eKalcXUtPkpz3aceOVJJKQcaQOpqVNVVSU3bdo01cGYlqqqqlBxMzgVN0NTcTM0FTdDU3EzNBU3Q1NxM7SeuBkqk3g6J/bVdTM0FTdDE0JsllJWDbZsetU7KYqiKIqiKIqijDOV6VEURVEURVEUZUZTmR5FURRFURRFUWY0lelRFEVRFEVRFGVGU5keRVEURVEURVFmNJXpURRFURRFURRlRlOZHkVRFEVRFEVRZjSV6VEURVEURVEUZUZTmR5FURRFURRFUWY0lelRFEVRFEVRFGVGm5JMjxDCIIT4nRDiZSHEt6YiDIqiKIqiKIqinB6MU/S7lwLHpZTvFUI8LoQolFLWDrdB2W0Pj8sPV9jhkH/k6y9ywTWrPPzy6Q4aEt89f+tZPL27nofeOEAgDF952yqcFhM3//plbl5swpFbwpJCK1+8exe7I/DDt1WSYXeS5rCS6TIhgZgu6Q7GmJ3twBvW+dzfN/HRdZlULV4AgK7rtHWHSXdYaPUFcdvMWE0GSGwb0yVmozaucTORnECuEw75Bi6zAhlmaAvHPy/Oh5x0F23dXgJhKM30MLfYQ1dQ51irj6JMFy0d3ayclU3VrBzMBo269gB17d0sLHST43bgsJmAUyNuJlv1HZcDvXHT83dfwy07lbddAPxvkOXHW7vYU9fFhYuLUvY1Fi7Am/j8nTfP4/b/7AUg3wjZZtCt8KELF/GRf+zEBjz6mbP5+6uHKM1yk+228tcX9lKcmcaKkkyWlKYRkpBhMXCsM0xBuo3d1U1gsrCw0IPDqOGLxIhGQxxsDJDpNNPpj5LjNlHbESTNaqTBG6bQYyE7zY5BE3QHY1jNAofZQnt3EIHE47DhtpsIR3V8oQgOkwFD4vkSjeiYTIYBz5v+8TzWczOWZcNt83+PP8NDm/w8c/v4hWMo/bcZyz4mQtltD0+LMMDAuBmpV287H18wyAU/fin53Sfz4McNA9f9+/tWUZHlYPUdzw5YNprf//UNy9kwKw2Hw0E4qhOO6jit8WTSL57cw9uXZnHP1mYyM2P8++UWNh1PTUy8fvt5pFk0mrojZFottAcjFGbYB/yO3+9na20362ZnnzBMp7vpck9NB16vlxePdHHpkkLg5N5TBQZwOiDHZaAiN50VFbkYNUEoEiUYjWHSNNZW5hDSJR6rGY/DQn2Hn+5QhAynDYdZwxuMkptmQ9d19jV4Kct0ENV1QGC3GAlGYkR1iUBgMWqYjBoGTQwankhMRxNiyOUAUkrCMR2L0TDg7/7LhiKklGOOtLESQnwXeFhK+aIQ4nZgm5RyyLNnyZ8t82/68aSFbzJpgN7n72X5Jv79sQv4xD1b2VLTgT8SQ9cl2S4zn7loLhtmZ3PP6zX4wzEuW5zPwqXLmalxM1YZNgN/eM9aVq9epeJmCPV3fzIZN0MlKHuMZvmpuO2fXjrEVx6KZ0ycFgMH7vrYKXPdaAIEEBvDY7zvs8digLPn5LCrvov27jAuq4nybAdIaOkOMyfXyUfPnc3ylStT4maoBOVIz814LOv7/ezbHiYyzr81lP7b9L2nRrqPiTBU3ExVGGBg3Ex3r91+Pl+8fwf+cIwPnDWLm/6wccTbWk0CgSAU0TEZNa6tKuYbb1mUXL6zpoUr7nwNgAyHCe0/t7Np06YhE7Cnc2Lfkj97WtxT08HWY6285RevAmA2CI7+/hOTdk9pAjIdJpp98aerAMxGgVHTWF2WzrbjnbT5IxgEuG0mbGYDOU4LvnCUpq4QALNzXVwwP4cbzyjDaUmtbzne7ueBLbUYDRpvryomw2EeEIZoTOfeTcdp7Apy1pxslhV7+OemY9R3BjljVibVrd3UdwbZMDuL1eWZm6WUVYMeyzjHzUhlAu2Jz11AWv8VhBC3CCE2CSE2xfydkxq4yaT3+3trfYQOf5T9jV6isRhdgQiRmE6TN8z+Rh8NnUG8wSgxXXK4eZBqEwVvMMbLh1qmOhinjNO9Nuw3LxxJfvaFYlMYktHT5dgyPJD67AnHYFd9F95glIgu6QpGqGsPUNvhJxCO0tgV5OA0ed4Md71Ghlxyejjd7+Xx8q/XqpPv2der20+8QR/BiCQS09GJl0S/cKA5ZflvXj6a/NzefbpfscpI/e753vdUeKwP/THSJbT4eq9VCYSiEl1KdtV30RGIL4tJ6A5F6A5FOdYeoL07TDASIxzVae4K0tgVoqEzMGD/1S1+IjFJIBzjePvgTbG6gvH3EMDBJi++YJT6zvjfO+s6k58PNA7/npqqTI+XeCsQADfQ2n8FKeVdUsoqKWWVwT4gT3RKE33+d5hST8Hn1meQ4TRz/vwc0h0WZmU5yLCbWFjg4oyKTEoy7JRl2cl0mlla7JnsoJ8SKnKcvGlJwVQH45RxMqVnU7XtyVjQ7+8fv2NZ8nNF1sCmKNOVAOwmDZtx6OYA/dfvWVMTYE1sJ4AMu4FLF+ZRlmknzWqkPMvBitJ0qkozKEq3s7TIw/J+z5u+528yz+Vwv7W+fKpabKeaqmv7dC4JHy8mTXDTmZVUZDvIcpq5fHEeI7zFAMh2WkizmbAZNSwmA+9eX5ay/MuXzifRUpSF+a6BOxiFstseHvKfMrN858q5yc/ZDtOk/a4mwGbUWFToSr4/zIZ4zY/TYuSKpYVUZjswaPGWEgVpNorTbawtz2B2joscl4Usl5nFRWksKnRTkuEY8BsLC9xkuywUptuozHEOGo50u4mFBW7SbCaqyjJw24wsLkwjzWbirNlZyc+rytKHPZ6pat52PVAupfy2EOK/wLuklG1DrZ+ZlSWNaTlEYhKLQcMXimIzGyjp01a2Oxyltj2AQQjy0qyEozo2syHZD+ZkNXtDhKI6GQ4zdvP47HM8VFdXU1hcQl1HECkleWlWrCYD/nCUtu4IVpNGltMy5PbRmMQbimDStGTb5VBUxx+KYjEZBj1WXUq6AlE0AS6bif7vg65gBF2XuKymYdtnDqbVF6LFF8ZkEKQ7zDjMxmRfgtGqrq4mK7+ISEzHaTGiCYE3GEGIeJikBLct/v1QThQXPboCEVp8YSwmjYI0G4Pt0h+OEYrEsFuMWIwagXCMQCSGLiVGTTthWAYT1SVN3iBIsJoMGDSBy2qiJ9p1Cd5gvBTGbTUlw7Vr30Fw5WA1aVRkpz5kJOANRNCHiJ/27jBt/jAOi5E8t3WIbUd//nUZr+aOxiT5aVZsU3Sfbd1zAIM7BwEUptswahou6/RITE+16upqysrKRrVNNCbZ3+hFR1KQZhu06cJg6joDtHdHMBsEs3NTE4f976XpYNe+g0hnNmaDxpy8k0vMnoza9gC+UBSP3URuv/tzMh1vDxCOxsh2WWltOJ5y3TR2BYnEJFlOC9ZEwV9XIEJXMIrTYsRjjyfqYrrEG4ygCZHyrjnW7icSleS6LRg0QYsvjNkoyHFN7PEGIjGC4dhJpy1q2wOEojGyXRaOHKlGJJ43iwqnroB3vI5tPI3leXO6qK6uprS0jCOt3fhDUQBsJgOlWQ6MmsAfjhFMpC9MBi2ZLjiZ9NmpYvPmzVJKOeiLYdLe5EKI9wEXAJ8A3g1UJTI/vxouwwPgySmg8KYfE5OSZm+ITKOGAP70gTNYUBB/SFx31ysYa+PN4Oblu1hXkY3FpPHhcypPOuwHG7188YGdAJRm2vne1UtPep/jpaqqihvv+Dv3bqoB4IxZmfzg7cv4zL1bOd4er0b83tVLKM0cmLsGeGBLLUdaugG4fk0JuW4rv3/xCJ2J6soPn1sxoGPYCwea2ZSo8r9oYS4LC3of1IeafTy4tQ6IP8AvXJA7quPZcMfTOAJhwlGdtywrZGmJhxvWlI5qHz2WrVjJTf/v7wAUZ9hxW43squuisSuI2aiRbjezdlYmZ1RkDrmP375wGG8wihDwkXMrMRkG3kdSSq759SvUdwTQhOC7b13Mhn4dVEPRGHc+cwgAj93Eu84o42dPH6CpK8SxNn+8VL0snTNH2bH17peP8L8dDXiDEfLcVhYXedgwO4tVX3+DmwAAjSBJREFUZRkAbKpu44UD8aZ+Z83JYmVp/Htr/mwKbvoRQgj++/mzyEvrzfjsquvk8V2NAIOG6ZIfP5/MSN3/4XXkuG3JZfsavPxvRz0Ay0o8nDs3Z8TH8tdXj3Lnswfj2xan84sbVowqLsZL33bkly/MozzPxdUriygepEPy6aaqqopNmzaNapubfv8aHfvj16DHYWLTly8a0Xbzv/wo1mi8ueG3b1jOxYvitbeRmM4vnjmIlOCyGnnfmbNGFZ6JYs2fTV7iuvnhzVWcPXd0z77x4AtGWXfHU2RJidloYNOXL5z0MAC8eKCZz9+3HYDSTAf7f/3R5HXz2uFW/u+J/QAsLkrjS5fH615v/sPr+MMxNCH483tWYzRqPLO3ia3HOgC4fEk+c3JdPLqzgW/+dxcAlTkuyrMc7KnvAuBzl8xjZenwJb0n46dPHSCmy5NKW7xysIVb/7UNgOJ0O9Vfe1fyeXPlGSV85c2Lxyu4ozIexzbexvK8OV1UVVVx26/+ze337yCcaJVt1ASfumIB164q5pfPHqK+M0BTV4ilxR7OnJ1FusN8UumzU4UQ4o2hlk1KEZkQQgNuTPz5WeC7QCngk1L+7ETbW40GbGYDAkGmI17a47SaUqrJlhV5EEJgMGgsL44/9Ao9tiH2ODq5aVbSEqOBzc4dvOptKs3Pd2ExGtA0wfwCNxBv4gXxKsFs19A1PYXp8ThyWY3JY+z5LtdtxTxIIj8/UZNhNmoDStaynBYsJg0hoMAz+lK30kTzIovJQLrddFLn0CAE7sQxFXisFCT2leEw40rU/OSnDR/Goj5xYRyiVEQIwZxEfDutxngH8H7MBo0ctyURFhsGLf7bdrMBjyNeA5OfNvpjnZvrwqAJ7GYD2W4rBk2k1L7kJr4zaIK8PvvvKeGxGLWUDA9AjsuK2agNGabyrPjxZTkteKyppfbZLkty29Geu+XFHkxGDSEEiwvdo9p2ouSlxWv4Rlo7oQx04fyc+IALAiqzRv78zHbF49xk0Fha3Hs9GDWRrMHouT+nAy1xTxk1wYJ8z5SEwWntrSU50bNtIlXmOLGb42Wq8/rVepVlOZK15nP61OD1FMwVZdgwJmrvCjy975qeFgsLC1zYEjURCwrczE3s32ExUDzB10PPO+Rk3ksVOQ4cibjpn564csXUNcsej2NTJteiIk/yPoP4+3xurguzQSPbZcFpiafrDIln5smmz2aCSWneJoS4DpgNLATygXOllLoQ4nHgKill93DbV1VVyYefeoHW7jCzMm28dKiNpSUespypJ+316lYy7RbKshx0+MOk283JF9HJ8gYjNHlDA5oCTbWekpCjLT7CUUlFjjN5zAcbveSl2ZLN1obS3h3GbjGkDAPY1h0mzWbCOEimB6AzEMFkECk3XI9AON5xLc0+tnanT+1uYF6+G6vJQOYwTfNOpKqqipdefY3uUCyZaO30R7CYtPiw31Litg4fxpHEBcSbYew43kFJhp2MIcIcjel0BiJkOMwIIZJ/W4waMUky0zlaDZ3BeP8wqxGZaFbWV0+Tvr4jpqxYsZJP//xfXLAwe0CmB8AfjhKJyUHDFIvF2FLTwbw8F07bwMxAIBwjHNPHdDy17QE6/WEWTGEzjxUrVlD18V/y4fMrKct0YTVNn+YeU22sJa8vH2imps3PdaOstf3Ly0fYMDubsn7P3f730nSwcuVKbvnBPVy8KJey7KnLtHd0B9l8tIP1s7KwTmGzzFZfiKOtiVrsftdNpz9MRyCS0gIhGtWpbu2mJNOR0qS5MxDBbNBSmrs2dQWo6wiyrCRewHmk2UeGw0yafWILJ2K6HJe0Rf+4WfHRX/CO1cWcu2DqMj3jdWzjqee6USPbDdQTN8fb/Lx2uJVgNMZZs7Mozow/KyMxna5AJHkv9aQLTjZ9dioQQgw5etuEPxGFEGbitTyfAL4FmKSUPQMH9YzcNiDTI4S4BbgFoKSkhNw0G7mJUufzF+QN+lury3qbKZ1MYnkwLqtpQGJyOikdpAS1Mndk7crT+5ViCyFOGH/DJWhtZsNJ9ccY6vyOhcVoSGmeN9obfSRxAfGak54X8FCMBi1lX/3/Hqu8E5ToDnbdaprgnevKh9xmsMxsD4PBQFX50E0CbWYDNsZ2/gvTbcmaxqmiaRp3vXvNlIZhplk3O5t1Y9huqGt0vO6d8SSE4APnzp7qYOBxWMf1GTpWmU7LkOcozT4wg2I0aoO+swZ71+S4bSnNassnqTDSoI3sfXAi/eNmOjxvxuvYlMlVlGGnaJCm16YhnpEnmz471U1G87YvAD8Hoom/ddFbNOcGBu3P03f0tuxsNYGXoiiKoiiKoihjMxl135cDZwA2YA6QB6wTQmwHkFIGJyEMiqIoiqIoiqKcpiY80yOlXA0ghCgD7iBe8/Nn4qPb3jbRv68oiqIoiqIoyult0no5SimrgesSf26YrN9VFEVRFEVRFOX0Nj1mdVMURVEURVEURZkgKtOjKIqiKIqiKMqMpjI9iqIoiqIoiqLMaCrToyiKoiiKoijKjKYyPYqiKIqiKIqizGgq06MoiqIoiqIoyoymMj2KoiiKoiiKosxoKtOjKIqiKIqiKMqMpjI9iqIoiqIoiqLMaCrToyiKoiiKoijKjKYyPYqiKIqiKIqizGgq06MoiqIoiqIoyoymMj2KoiiKoiiKosxoKtOjKIqiKIqiKMqMpjI9iqIoiqIoiqLMaCrToyiKoiiKoijKjKYyPYqiKIqiKIqizGgq06MoiqIoiqIoyoymMj2KoiiKoiiKosxoKtOjKIqiKIqiKMqMpjI9iqIoiqIoiqLMaCrToyiKoiiKoijKjKYyPYqiKIqiKIqizGjGkawkhHgByB1sESCllHPGNVSKoiiKoiiKoijjZESZHuAXwFuAz4/lR4QQOcDvgCzgOeBHwJ+AdODbUsr/jGW/iqIoiqIoiqIoJzLSTM+9wEeBdill1xh+xwa8W0rZKoR4Grgj8W8T8CSgMj2KoiiKoiiKokyIE/bpEUJcKqXUpZQbAOtYfkRKeVRK2Zr4UweqgOeklF6gUwjhGMt+FUVRFEVRFEVRTmQkAxncKYQwJz6/eDI/JoT4GLAf8Eop9cTXXUDaIOveIoTYJITY1NzcfDI/qyiKoiiKoijKaWwkzdt+A9QLIVqBUiHEfhIDGDCKgQyEEN8FTMSbyT0vhBBSSgm4gbb+60sp7wLuAqiqqpIjPB5FURRFURRFUZQUJ8z0SCm/I4T4MZANPAtcQDyzM2JCiLMAk5Ty1sTfu4F1Qojtid8Iji7YiqIoiqIoiqIoIzOigQyklH7gqBDiw8AfgUJSMz4nqulZB1wihKhK/P1n4P8Rry26bTQBVhRFURRFURRFGY2Rjt7W4yfAp4BPA7cCFzJIf5z+pJQ9o7X19btR/raiKIqiKIqiKMqojWQgg5T1pZQPA1sTn78HXDvuoVIURVEURVEURRkno63peU0IsQH4L3CXEOJ1IDD+wVIURVEURVEURRkfo8r0SClv6PkshLgdWAR8d7wDpSiKoiiKoiiKMl5GW9OTJKV8DHhsHMOiKIqiKIqiKIoy7kbbp0dRFEVRFEVRFOWUojI9iqIoiqIoiqLMaCrToyiKoiiKoijKjKYyPYqiKIqiKIqizGgq06MoiqIoiqIoyoymMj2KoiiKoiiKosxoKtOjKIqiKIqiKMqMpjI9iqIoiqIoiqLMaCrToyiKoiiKoijKjKYyPYqiKIqiKIqizGgq06MoiqIoiqIoyoymMj2KoiiKoiiKosxoKtOjKIqiKIqiKMqMpjI9iqIoiqIoiqLMaCrToyiKoiiKoijKjKYyPYqiKIqiKIqizGgq06MoiqIoiqIoyoymMj2KoiiKoiiKosxoKtOjKIqiKIqiKMqMpjI9iqIoiqIoiqLMaFOS6RFCGIQQvxNCvCyE+NZUhEFRFEVRFEVRlNODcYp+91LguJTyvUKIx4UQhVLK2qFW3lHbSdltD09i8CaWAGTif7tJozuiJ5etL9C47uyl7Krt4Ln9LcR0SSAcpTjTzqcunMvSIg8P76jDG4xy8cK8GRc34yHDZuTBj52p4mYY9X3ipvqOy1OW9Y+z4ZZP1bb9l49m2znA432Wbz7awtt++RoAFVn2U+q6sZs0dCkJRuWI1u959mgCzAZBMCoRQIbdwFuWF/N6dRv1HQFy3Fbm5LoQQG1HkIWFbt6zvjwlbkZ6bsd63ka6rO/3N/z6MV46Eh3X3xpK/22Gu6cm03D32FSEAVLj5lQyK8vO4Rb/qLbJdlrQpY4/FEPTIM1mpsBj40fXLqU4w0GXL8SGHzxLdyjKhQtyBzxvxnLOxuOcj8c+zvre0zR0BllbkcGf3rN21NsHAgGWfusZwjHJsqK0MYXhdDFZ76n4u0Ijx2GipjMEgNkALqsJTQiuWFrASweaOdTSjc1kIMthxmk14jQbqe8K4Q9HsZg0lhZ5OGduDm9eVojZmFrf0t4d5uEd9ZgNGlcszcduHpg1kVLyxO5GjrcHOHtuNrOyHDy1p4maNj8bKjOpaQtQ0+bnzNlZwx/P+EXNqKwHnkh8fhZYNkXhmBKyz/99MzwAL9Xp7DjeyeO7G2nvDnG4pZs2f4RddV5eOdRKTZuf6hY/rb4w2451THbQTwltgSgPba+b6mCcMk7mwTlV256M/f3+/uTftyY/HxplAmeq+SP6iDM80Pvs0SXJ7STQ5o/x6K4Gjrb66QxGOdLSzRtH29l0tI3aDj/bj3ewpd/zpu/5m8xzOdxv9c3wTKWpurZPxYzFdFY9hudBsy9EZyBCIKrjD+s0dgWp7wxw3+bjAPzm5cN0BaPEJDyzr2m8gzxl18Dm6jaOtweI6pLXDrePaR93PH6IcCz+XNp2vHM8g6eMUfxdoSczPADhGLR2R/CFojy8vY6Dzd3EdPCFYtR1xjMf22q7aPIGaesO0+INs+N4J7vquqhp6x7wG7vqumj2hqjtCHCwyTdoONr9EXbVddEZiLCpuo2uQJQdtZ10BiI8f6Al+Xlj9fDX3lRlejKBnpB1AQOy9EKIW4QQm4QQm2L+mXvxayL17xwj5KdZmJfnwmg04LaZMBk0clxm5uQ6yUuz4rIaMWiCWdnOqQn0NGcA1lUMn9tXek1lqfR08P4zy5OfnRbDFIZk9DQBBnHi9Qbdts9nswEW5rtxWY2YNIHbaqIg3UZRuh2b2Uiu20rlNHneDHe9miYxHNPR6X4vjzendfSNYawmgcmgoQEGTWAzGbAYDZwzNweAty0vxKQJhGBC3uFTdQ0sLnDiMMefn4Ue25j2ceO6YnoeZ9kuyziFTDlZmgCHufdFIwCLUaAJwcJ8Nx5b/MlrEOCwmHBZTeS6zVhNBsxGDbNRI9ttJddtIS9t4LVRlmXHZBDYzAaK0+2DhsFtjb+HACpzXDitRgo88b8XFaYlP8/OHf6eElKOvJRwvAghfgj8U0r5qhDidmCzlPKxodbPysqSZWVlkxa+yeQPx9ClxGE2IsaQeKmuruZUiptwVCcU1bGZDBjHmloboVMtbk6GLiXdoRgmg8BqOnHC/WTiJqpLAuEYlsTDbKYZLG66Q/EaBIdlqloETw9HqqvJyivCbNSwzMBzfzJOp+fNSAQjMSIxicNioObo0QmLm55nn9EQz2CcatQ9NbRk3Bg0LCYVN32p583QNm/eLKWUg14wU/UG3wycD7wKrAN+NdzKZWVlbNq0aTLCNamOtnbz7zfiXZmWl3iSJUGjUVVVdcrEja5L7nz2IJGYJM1m4j0byk+80Uk4leLmZP13ex0HGuPVwu86o5RM5/ClZCcTN39+9Sgt3hBGTfCBsytmXManf9xsPdbBM3vjzVDOnZfDsmLPFIVs6s2av4QP/uheDJrggzPw3J+M0+l5cyKtvhB/euUoAJU5Tr7+3isnLG4e3VnPnnovANevKUmWBp8qKhbE7ylNCN53ZvlpX7DSV0/cCAHv3VCOy3q61+X2Us+boQkh3hhq2VS9se4DlgshXgAek1K2TVE4ppTNZEjW7gzWcWumEYJkLYTjFGtGNN3ZE80KTAYx4QlRe+IcWkwahv7tM2egnrgFks03Tlc9p9tq0gY0zVWUHmajhilRk2+f4Hum591p0MQpWVOiJRIBZqM24a0fTjV948ZkOPXOrTL9TEpKWwiRA/wOyAKeA34EuAALUDMZYZguojGdFw+2ICWsr8ziulUl+EJRKrIdUx20CSeE4NpVxdR2BCjLHP3xtneHee1IKzluKytK0icghJNPSslrR9roDERYX5mFc4ylfGfPyaEo3U6Gw3zSpWG6LnnpUAuhiM6G2VkDmstdsTSfIy3d5KfZZmSmJxLT+d+OeipznMzJdTEn14V1RTwOSjIHb298urCbjTgtRpaXeDCqRIgyBJfVxIICN9Ut3Swt8kzIb0QS71JdSi5elEuW04LHbp6Q35pIdrMBp8XIokI3FuPpXajSn91swGU1Mj/PPaJm24pyIpNVvWAD3i2lbBVCPA3ckfi3CXgS+M8khWPK7arrYktNBwAuq5GqsoypDdAkc1lNzMsbW6L8+QPNHG7uZk+9l5IMO1knaMJ1Kqhp8/PKoVYgXqp14YLcMe3HoAnm5LrGJUz7m7xsSoyAYjMbWF+ZOiiExWhgXp57XH5rOuoKRNjX4OVAo4+yTAdmo3baZ3Z6eENRfKEoLx1sZXFRmkqkKYNq9obYdiw+ANFLh1om5Dd21HayNfEuTbebyXGdWs3aeniD8XvqtSNtLC7yjLngaybyhaJ4g1Fer25jUVEaaTbVvK2/oUbrU4OaDG5SiuqklEellK2JP3WgCnhOSukFOoUQM7+aI8FjNyWbtJ2KpVJTqSe+LCZtwptMTBaX1YQxUVuS4ZgeD/Q0mynZrMBjnx5hmkwGLf5YdNuMyXOjxPXEh9NqxKipmh5lcHazIdnxPH2C3nPpdjNCxJtNe07hxHDPPeUwGzGr2tMUPS0JHBYDVjWQgTIOJrVIQQjxMeLTZNillD0T1PQMWd3db91bgFsASkpKJjOYE6o008H1a0qQklOuw+VUO2t2FrOyHKTZTTOmD1SGw8yNZ5TiC0UpGmKoxsmWn2bjnWtLCMd08gcZXnKmS7ObeOuKQnJcVjSV6UnhtvXGzUxs2qiMD4fFyDvXltLpj1CUPjHPkPIsB9evjqcNck7hd2nPPZXltKiBQfpxW028bUURGU6zqlVWxsWk3WFCiO8CpcBHAV2I5ADNbmDAQAZSyruklFVSyqrs7OwR/ca2Yx3ct/k4R1sHTn40neS4rKd9hkdKyQsHmnlgSy2tvtCJNyDeJ6g4w457Gozg8sqhVu7fcpymruBJ78tjN096hicU1blv83F21g4+B1am03JaZngAIlGdTdXtHGjyTnVQph1BvODGNkhN6xs17fz7jeMcazu1JnhVJsbO451sOtpG8wif72OR47aOKMNT3xng328cZ2P19BszKRLT2Xy0nb0NXVMdlGknEtPZdLSNPfUqbpTxMVkDGZwFmKSUtyb+3g2sE0JsB5BSnnTKMRSN8cy+JqQEbzDCu9dP7HDIysmp7wwm+40YDYIrlhRMcYhGrsUX4tXD8daaut7C21YWTXGIRs8bjFDT5udYuz8+Ea5qVpHUlRI3blX6OgL+cJTn9jUD0B2OcePa0ikOkTKVmrxBXjsSz2AIWk+w9sR7fn8zdR1Bjrb6mZPjIm0aNdv1BqMcbfXHw5brUsMy9+HrGzfT7Lwpp6bJepuvAy4RQjwrhHgWeA34f8D/gK+Pxw+YNI1MR7ztcF7a6V2LcipIs5mSpcV5p1itl9NiTHY2zT9Fr7We4T+zXRbVTKmfnrjJdFqSw+4qw7MYDWT0PH9PsftZGX8uiyk5LcF0aNXQMwu8u897Z7roed6k2UxqhLJ+egrjpuN5U05Nk1LTI6XsGa2tr9+N529omuDaVSV0+MMzYlSvmc5hMXLTGWX4QlGyXafW+bKaDNx4RildwcgpO2JQms3EDWtKSHeY6W1pqkBv3HjsKm5GyqAJrltdTKc/csrdz8r4s5kNvOuMsmnzjDxrdhbz81y4baZpV3Prshq5YU0JaXaTmoumn564mY7nTTk1zYze4Almo3ZKd2g83djMhlO29MZqMpzypXLqXhmaipvRsxgN5LhP7XtCGT/T6RkphJjW9/R0DttUU3GjjCeVdVYURVEURVEUZUZTmR5FURRFURRFUWY0lelRFEVRFEVRFGVGU5keRVEURVEURVFmNJXpmaYONvl4Yncjzd6Jm9htpjja2s0Tuxup7wxMdVAmXYc/zJO7G6fN5G1SSjZWt/HsviaCkdhUB2dMojHJE7sbOdzsm+qgKGOwpaadZ/Y24Q9Hpzoop7WdtZ08taeRrmBkqoMyIXRd8vKhFl440Ewkpo95P1Fd8uTuRvY3qsmQ+4sl4kZN3KqMlxk1ettMEYrG+N+OemK6pLEryDvVRH9D0nXJQ9vqiMQkNW1+3rvh9JqU9qk9TdS0+dlZ10mBx0aabWonbzvU3M2LB1qA+DDGZ87OntLwjEVnMMLO2k721HfxoXMq1DCyp5DajgDPJiZJjeqSCxfkTnGITk+tvhBP7G4E4pPVzkS767t47XB8AlabyUBVWcaY9uMNRNhR28nOuk4KPTYcFpUs69EVTI0bNXGrcrLU23waMgiBLTHUp8uqHoDDEQLs5ngcuU7Dl4UzcX2YjRqWaTCPgdNipGdqG+cpej565iO1mw0Y1Dw9pxS7yZCcbLdnckxl8llMhuS8KjP1udz3+XYyGRUtcb1aTQaMajLkFD3PX4vRoAqflHExM59GpzijQeO61cU0dgUpyXBMdXCmNSEE164qpq4jQHGGfaqDM+kumJ9LRbaDbKd1WsyJkZdm5R2rSwiEY5RlnZrXbprNzBVL8sn32JIJEuXUkO4wc/2aEroCEcpP0etvJnBajFy/uoTW7jCzZuh5KMtycO2qYmK6PKl3j9tm4k1L88l2WbEYp/4ZPp24euJmmrzflFOfyvRMUy6rSVXljpDDYmR2rmuqgzElDJqgMmd6HXvuKT6ZnBCcttfTTJDltJDltEx1ME576Q4z6Q7zVAdjQhV4bCe9DwHT7hk+Xai4Ucabqi9UFEVRFEVRFGVGU5keRVEURVEURVFmNJXpURRFUf5/e2cdJsl13e33Ns909zDvzjLvakErZgssy5JllNmJKQ7Dl+TL5zDbjuPEDpkdO4aYLdmWZMlirbSSlhmHGXqasbqq7vdH1cz0zPQwz9a7zzzbUN11q/rWrXvOPed3LCwsLCwsVjSW0bPAZFSNJ8508/jp7mVbxyQfBxsDPHy8g75oerGbsuB0hlP85FgHR1uDi92UOUHVdJ4618vPT3aRyFi1TtJZjV+c7uYXK+yanQmKqvPjox2cbA8vdlMsljgHG8x7Qmx+7gkr5brMavqKun/MJYPn5nCLdW4s5oZpCxkIISqAdVLKI/PQnhXPyfYwj53qRgJFHie3bK5Y7CbNmv5YZqheAQTYs7qE050RttUUsbVm5SchvnCxn95omtaBJFuq/ZMKUBxuCdIVTnHTxgoq/YuTcK2oOj890cnmKj876opGvHepN86ZzggAJYXOZVlrZzYMnpuNlT52rSrmbFeECz1G4cCqIjf7186sHsdKIJRU+OW5Ho62utha47cUlSzy0hdL85NjncQzKvH0/DhOTrSHeex0NwClXic3bFie99JwMsuTZ3soKXBN6f5xJRFJZfnl2V6KChxsqfJTXGidG4vZMS2jRwjxMeAPAS9QL4S4DXiLlPIP56Nx0+VgQ4CG/jjXrS9jW03R5B9YBDqCKV5rDiIl3LChdEUYPT63nd5omv5Yhk1VXr75SgstA0kqfQFu2VxBIqNxz47qRZvgzzd2YVQfX1VaMOkkMBBL882DLcQzKn3RDL9224YFaWNnOMWzF/qo9Ll4/Y4aBhIKDx/vpNLn5pNvuwpHTg2E4kIHjX1xFE3nji0rw+BpCSQ4cLmfVaUFvG5rFWKC+jt9sQz/9VwDtcUFfOVXrqHK78EmBF3hFC9dDuC029i9umThGr+ESCoah5tDlPmcSF2OeO/A5X6aAwlu3FBuqd9d4SRSWZ671EtK0UHKyT+Qw3MX+ugIJbl5UwUbKn3jbtcaSAw5227eWL5sjZ54RuXA5QA+t4P/d9/WxW7OkiKWVnnxcj8+t50/uXfbYjfHYgUw3fC2PwL2AXEAKeWLwANz3aiZkM5qPHexj7OdUZ6/0D/lz/VF0xxuCRJNZ+exdcN0R1PYbQKHXdAbySzIPuebWEalzOtifaWXrCbpjymkFI32UJKm/ji90TQn2sO0DiQ42hoknVW50BPlcm9ssZs+J6i6ZH2FlyKPg1RW43JvjAs9UWSem30qqxNKZkkqGv1x4/fX9elNCqZLSyDBD4+00xNJcb47Rnc0jarrpBSNQDyDPqqd4WQWn8eO322f8+sinlE53BKkJ7KwYZCvNQ8QiCucbI8QSk58TClFozeS5lJPlHRWpb6skF+9cS1lXhe6hJcbBhao1UsPTZe4HAJF1UeEFCUyKi9c7OdsZ5QXLk19/J2IpKIOrYpOl/m+piwmpqE/QTKjoWoazQPxKX8unFQ40R4mEFd4rXnikKbeaBpp/uudJKw6ndU42R6edLuJmK8+pag6uq6TVFQ6Z9DXwRhXT7SHCSaUOW7d4qKoOqqmEc+otAUTi90cixXAdMPbFEADJIAQohJYEu57u4DOUIqBhDLlStyqpvPDox0oqk5DX5z3XLdmXtv4xJkeOoIpijwOiguc3LG1al73txBIKTlwOcDZrggVPjf1ZYXcv7uWIy1BNlZ5SWQ0UopOmdfJI8e70KXkSEuIpGJMmB7YvfxroqwpL6QvlqHC56IjmOTJs70AqJpk16riEdtWF3m4Y2sl3eE09+6s4cDlfo60hNhW4+e+q2rnvG3BhMIjJzrpjaSJZ1SuXltKuddFscfJzroitlb7cY0qiBdKKLzaFETTJbvXlHL7HLbnF6e76QilcDlsfPTW9QtWjG9DpY+ucJoKvxu/Z+JhT5fS6J8CnGZx0hKvi+11RTT2xa/oopd+j4OaYg/ry70UFQyHmtiFoCOUJJTMUjJHIShPnu2hJZDEYRN89NYNFLgm7yvprMYPjrQTSWa576paNlWNv1JgMX+UeV2oukTTwKi2MjV8bgeVfjf9scykRU3rywpxOwy/7WRFvJ8610tDXxynXfCRW6bWl3I51RHmuQv91BZ7ePv+1djnsGix3SbQdHA5BDX+mdU4+9mJLnqjaQpddn7t1g0rqqiyqgECSiYZty0spsJ0e9GngMeAciHEPwNvB/5zzls1E4RgS7WfUFJh4wRL4nk+tiBIKbnQE6WqyMN168p48746rl5bujA7nwJSSvrjGYoLnNOaiGZUnbaBJBsqfKhSZ8/qEpx2we1bKil02dF0iapL0lmNly4PmPsa/nxWWziPbDpreIzmunDhrZsr2b2qBK/bzqXeYa+mmscz6HLY+NDN68moGoUuB19+sRGACz0xXr+zZszNtDuSoj+WYVtNES7HzHVHqoo83FLt5w27jH34PA5ev7OGLXkmhbG0SqXPjQQyWX3a+4pnVLKqvqQKE167royddUW4HfZJJyw2m8DvcVDgsqNoOoPlB9+0u5akolFoTpgSGZWMqlO2hI5zvqn0u/mdOzezq7YIe05IJAK2VhcRTilsqJxbo1CIqY/TPZE0A3HD232pN2YZPYuFgNJCJ4qqU13kYaprow67jfdet4a0OT5OxJYaPzdvLB96rOuSQCJDaaELp33kWKmZY7EuGbOyPRUudMfQpaQznCKcVCifw3uI22FjVamHkgIn0x9tDTTd+KSqS1bSGqfTLvAXOChw2tGnYTxbWIzHtIweKeV3hBCHgNebn31ISnl0Xlo2TezCCBmLpLJTTq512G28Y/9q2gaS855wL4Rg/9pSXrjYT0rVOXB5AI/TMWYlYJAT7WEa++Jcu66MNeWF89o2gOcv9nOiPUxpoZP337B2RI7HRHicdjZV+fjBkXbqSgp49kIfb9hVg9dtdC2HXeCwG9u9dd8q+uMZdtT6udATwyZgU9XUJ0inOsJc7o2zf20p66bpbU9nNb71SivxjMoNG8q50bxZzgWaLvF5HNhtgu21flRdN1ZJxvlt7TYxdEN3O+yc6ohwz47qMZPxaDrLD490oOmS7oixMjRdyrwu3rpvFYG4wq5VRUP7CCezvNI4wMn28BjP4O1bKznTGSGRUXlwT9209tcXTfOpX1wgldX46K3rxyT9v/GqWs53R1ldWrhgqzyDTDaJGsTjsGETUFfiwecZNmiEEEP9OpxU+M5rbSiqzj07qse9jlcakVSW0x0RLvfG+bVb1w+NE067DbvdeL9gjsQN7t1Zw7mu6JRy5QapLfaQVFT6Yxlev6N6TtphMX1qiwuQuiSjScp901v5O9oWoj2Y5MaN5dQWF4y7nQAcdqNf2AQ8fqabC90xVpUWjInauGdHNac6IqwqKRi6hqfD3jUlhFMKdSUFlBbOrZNDl5KBuIKuQ4FzZo6tB3bXcb4nyoYK35yuQi02dpsRSlvgsM25s9LiymS6QgZ/B/yblPK/zOe1Qog/klL+y7y0bhoomsbpzgjhZJaTHWHevn/1lD5X5fdQ7nUvyEBx6+ZKyrwufjkY/jROjHA6q/H8xT6khISi8is3rpv3tnVFjFjiUDJLKqvhn6LRA3Dr5goa++NIOexRy8ea8kJWlRaYxkER3z/czvMXA7x+ZzXbaycWnlBUnWcvGOckms7yoYr1U24fGJ+Jm/LLPdGZxU3noy+a5kfHOgB4aH89lX73UJK7rkuklOMmzQcTCsGEsTKZb8VL1+WQV1KdxYrY2nIva8tHGokS42abzzOYVSXFhU4KXHay2vR8jyfaw0Nx6S81BMYYPV63g2vWLW31s6ym43LYCSdVMlmNwjyTpEBcQVGNc9MTSV8xRs+gk1zTdXIv9ZSicrozSjSV5UR7mAf3rpr1vgpdDvatKZ3W2NwbzVDocrC23EF7KMnOK+R3WWo09sdRdIlDQEsgOeXPRZJZXrocAIzr8F3Xjh9yrulQ4XMNPX7pcoDmQILGfhfvvKZ+RL/xuh2zcnRtqfazZZ7CsBVVRwLRtEpPJE1RwfSNqlKvi5s2Lk8hh4lQNB27TZBWJV3hFCVX0Kq6xfwwXZfH+6SUfzX4RErZLYT4dWBSo0cIcS3wJeAmoBj4JlAK/KOU8qfTbEe+PdAWTNIVTuH3ONB0ySuNA6i6zk0bK8YNDfr2q62cbA9z765q7t4+fU/6dNlRW0RWMyaz460EuOw2yrwuBuIKVTOM8Z0uNUUenjjTw576kilLZgbiGY62hlhTVsiDe+rojWbYU5//mBRV58svNtIRSvH2q1fxUkOAkx0RdtUVc7kvPqnR47QLyn1uArEMNUXTPycVXjfRtEJTf4KbNk1/0n2pN4ai6uyoLRqxKtIaTA6FgLUOJIYU6i50R/nSi42UFrr4nTs35w2BKnTZ8XscxNLqiGN67FQXzYEED11TP+l5nSkCQ23r1k0VYyaV53sifPvVVhRNp9zr5j3XTz3XbU99MbXFHtJZnZuX6U04qxkra16XHds4tv/6Ci+7VxcTz6hcu8SNuLnE63JgE7B3TemIMVUIQdtAgp5omrI58oQfbAjwyIlOttYU8aGb1k0pT6HE68TttJHJGmFVFotDdZGbREZF1acXTlbgslNU4CSayk76+5V5HbzWbATOvXP/auIZle5ICodNmCIyw/2lPZjk4eOd7Kgr4u7tS2sFUEqIZzTcdn3OV5GWO4PnxmXXLblqizlhukZPXAixTkrZAsZKD1PIUhRCrMVQfhu8S/5fjPygI8DTwKyNnlhSoXUgiarpnGwPc747OlTQqtDl4Lr1YycmiUyWx093o+mSR453LYjRI4Rgb33JhNvYbIJ3X7uGcFJZsCXdX5zpRlF1jrYE6Y+lqZyCsfXM+V66wmnOd0f56K0bJpQXbQrEeelyAAl87unLZDWdgYRCeaGTd11bP+m+hBC865p6QkmFyhmck8ZAnPNdMSTwi9M9XL9+6hPyhr44j50y6kEoms7Va4ZzsbZU+7ls5vFsyQmRfPh4J13htHl+Ity8aaz0s8dp5/03rCWazg4Ztxd7onzrlVYkRgjaXzywY8LzOlMGEgr9sQw/O9nNb92xiYKc1Yynz/YOqRw9frprWkZPdVEB//DWq1CWca6Lohme14Si0RNJs65i7Pm32wR3LbHJ00IQV1R0CUdbQly7rnQoRDEQS9MeSqFqOsfbQ3Oyr4dPdNI2kKQ9mOKB3bVTMmKKPE4+eNM6kopmhcMsIs+c68VcCOVSb5ypBiO7HDbed/0aoqnspCUOvne4nY6Qsar83SPtBEw1zHAqayit5UREfv75Bpr6E7zSGGBXXRE1E4TNLTSKuZqe0STH24K8ftf0QopXMoOr6YomOdY6wKrS+Q/1t1jZTNfo+XPgRSHEjwAVeAfw95N9SErZCrxbCPG8+dK1wJ9IKXUhREQI4ZVSzkqP0OOy43XbSSvGUm+Rx4kQhqeguCC/h8DtsFNb7KE3mqa+bGldTC6HjaoF9FRW+T20B1P43M4p5z4UFzjpCqcpcNpxTRIOV+FzU+J1kUir1Ja4aR1IUeFz88CeVVM+9y6Hbcbe29ICFwUuB0lFnfZ35EpPj5ahLi5w8t48RsGWaj8Xe2O4HbYJa0Z5nPYR+QqFLgd2u0DV5KQqY7PBYRPYhJEkah/1222p8eN22JESNs4gEdzndiwRTceZYTPDEe02SzFoNHbz3Hjddhw5y2A+j4NCl51MljkzdteWFdIdTlHmdeOfRh5Gocsx5THMYn7YXOXFZt5/izxOtMk/MsToMXE8NlT4hgQLNlb4iCSzSGmIbYzOSfWZ/cdpty14LuFkDI03AuonUaG70sg9N5Mp9FlYTIXpChk8KoQ4DbwB8ABvllKensF+nVLKwWSBKEa42wijxyyE+jGANWsm9zT7C1y8fV8dz14M8Fu3b2RNeSHvuW4Nmi6pK8nv1XHYDSWtwy1B7r7Ck15/+3WbON4WYl2Fd8qJnvfsqGFLtZ9Kv3tSZbEKn5v3X7+GpkCCWzeX8Q+PXsBpF1yzQAp2FX43f/Wm7XSF0+yf5j43V/u5d6dE0fRxQxJH85Z9q7h6bSkVPlfecMGm/jjBhMJVq4tH3IRXlRSwqcLHxb4od26bP0nzVSUFfPiW9Vy/vmzMb/emPXX8+Fg7sbTKr926MMVTlxJVfjelhU7u2FJBic8KkcqluNDJm/fWUV3kGREWWe7z8ODuGl5uDPLbt2+ck32985p6/B4nu1YV582rsli63LWjlmvXN9PQm+ATb9nF/3t47vfx5r11RFJZpJS8ZV8dd22v5lRHmC01vjGhkHdureRke5iddUVLSlUSDPGNokInV68tYXudlYOWS12JB3+hk72ri9iz5soJI7aYPybNVhdCFOc8rgOywM+BHwID5mvTRRfD2d1FwJgqZFLKL0spr5FSXlNZOXlV+EhC4YmzfSQyKt98tdVo7ySfUTWdFy71E05mOXApMM1DWFl43Q5u2VzJ6mkuH09U2T6XUELhSGuIgbjCZ55s4EJPjNOdUb53pH0mzZ0R6yt83LypYspKULnsqCtib33JpHkFkVSW5kACgZH3kc/gMcLKujhwOTCUtDvIsbYQz17soz2Y4r+eb5x2O6fKoHT2jjw32a8eaOJsZ4zWgRSf+MX5eWvDUmUgoZBRNZ671E8osbBFVJcrfdE0z1wMEM+ofMMcf2fLSw0BggmFgw0BUsp01gosFpuXL/dzrjNOOqvxuWcuz8s+znXHaA+l6AinOdcdo7jQya1bKqkuGuvk/K8XmugMp3jsdA9N/VMvlroQ9MczKKrG4eYQl3uji92cJUVfzDg3R9sinO2MLHZzLFYAU3GffRV4yHz8MowQexLm8+m6g88BNwkhTgFIKWc9s3A4xFBRQQm0DST5yoFGNB1+5ca1bBsnUX5wzr6QKo+Xeg3N/63V/ikbDfNNVzjFoeYga8sL2bdmaishj53q4uWGAaqL3Lx9/2rCySzba/PXkxECBAKJxOO0IRAghsMOFoJXmwbojaa5aWPFpPHiMyGpqHzntVYyWZ3aYg+7VhWzvbZojFBA7k9uG/X7F7iMOjK6JkfIl7YNJImksmyv9U9ZTnwiFFXnkeOdbK72sXOU4ZNVJRlVRyJJr5DJZjqrcaEnRk2Rh5riiVdvdF2SUnRUTWKf3C90RRFKKHzisXNsrPbxJ/duH+rbTrtA043xd65GtMFrYzp1eiyWBgJBKquh6ZJYJjvm/fZgklBSYUdt0YzHM+OeZQgZXLe+DK/bwcn2MFuq/eyoG3m/HxxLHTYxVNB0qWBcNzoOuxwKH7Uw0HPOjRArqQKRxWIx6YxTSvlQztMHpJRn52C/nwS+hWEwfXwOvg+H3caG8kKaB5LsqC3iTGeYY21hpJTsrCsaY/T0RFJ85peXKC5w8MZdtWydRD1srrjYE+Px00ZSvKrJJSN1+/zFfnqjaZoDCTZX+6dkjLxwqZ+zXVHcDhuKquN22umOpHnDLkMQ4psHWzjaFuQjt2xg9+oS3nb1KgLxDBsrfXz/UCutoSQOm6ECN99Jx32xNK80GjdIXUreum9qkubTIZ3VyWR1YuksF3qidEfSRNPZMVKiFT43b9u3mmBSYWddER2hJG0DSXbWFbOzrpg/umcLjf0x3nvDOgB6I2k++YvzhBIKb9lXx7uvWztm3+3BJO3BJKtLCzjWFqa40MntmyvHXZnqi6X5zJMX2FTl56u/es2IicdVq4up9LtQdfIqk7UEEhxpDbGpyjepKMdS4enzvVzujeOwCT58y/pJQzgHJb2PtYe4dUvVGMP1ZHuYy31Gzaj106wZBUadn3NdUdZVeMcNv12KBBMKx9sjnO+J81u3b6S40Lhu3U47bhsMZLNUzFH40L07azjXHWVVydTr9FgsDVSMWmUSyI4qcByIZ/jxsQ6kNPrTHVvHhvFquuRbr7YQS6n8yo1rKc6jahZLZ0maZQhiSYXvH26jsT/BoeYgf/+WXSOu2b9+006+d7iNvatKFiwZ/ny3IeG+b5TS4Wik+adpctolAlY6uedGXRn+N4tFZrpu9seAdTPdmZTyDvNhM3DLTL8nH429cU53x8hkNc52RbhxQ7kx6EqZVzLzk4+f55WmAWxCsHtVCfsXQHb2aGuQZ873EUoaUtQT1bSZS7Ka4dUfSCjcu7Mm7yStyu+mN5qmuMA5ZU+Ypks0XRJJGbWRyrwu1pqFVJv6Y3z5QBNSSroj5/jBr99EfVkh9WWFdEdS9CeyNPcnKXA6KHA5eGD32CjJ1oEELQNJdq8qnnUctt/tpNBlJ6loVI9Spmvqj/NyQ4A15V5u3zJ5KOV4lHld3LW9itOdEZx2GxlV40RbmCq/Z0xl+DXlhawpLxxacclqkrZgkrdevYpgMgvCRl80Q5Xfw/meKKc7I0gp+eW5vjFGTzqr8cjxTvrjGeIZlXrzpr6hwqjP09AX56lzvVT53bx5bx0Ou41ISqU7kqYvliGZUSnKmVTY7IKkopnXz9jjfP5iH6Fklo5Qkm01/mUxIc2tyD7ZVTdUG0mHg40BfB7niNpCiqrznFlHK5rKsv6W6dWMAnj0VDf9sQzH28PsXl3MqY4Iu1YVz6r/LQQSQ8xDSh1dz5UETtA0kEICz13qm5N9Fbjs086/03TJk2d7CCUV7tlevaBiMBbDnGgOD11n3dEMuXdXXZf0RdOksvq4RcGfv9jHL073AEYo7q/nzRMTw84aIeiJpImmsnnvq680DnCsNUwgpnDblkpc0xyzWgcSvHg5wKoSD6/bWjUUodEeTKLpckyx7I5QkifOGO1Pq/qE1/XQ2AS0BOJsrZ2+IzQQz3CmM8LGSt+SE2WaDWrOubnUE2X3MnGyWSxdpmv0PCyE+CLwE2AoMFZKeXBOW5WHQdWsfOFg6azGz092oqq6ERIhYWuN4YWW0vBcj0ZRdVKKhhCChVjtllJy4HIAh03gcth43bYqrlqgVZ6eSHpI2vN0ZySv0XPX9ip2riqitNA1pIiTj9xim4PF3lqDSbKaTkcoRThphDL43E6cdoGiSnzukXktLYEkDptACCMHpjZPuFFG1fjZiS5UXdIZSuVVSJsOBS47H7hxLbH0SPU2KSWvNgUJxBUCcYW99SXjqv1Nhd2rS9i9uoQLPVEeO9VNUtF47FQ3H75lXd78HiEMiXI0icNuYyBuFCwFQyp716piaoo9rCotIJbKsitPDo4QRuzzpd4YAkMtqbrIM2QonumMkM5qtAWT9MczQ1XOdQlipLIrYNQYctptOOxwuS82Zn81xQWEklnKfe5JVfuWCndvr+Z0UYS64oJphVRquhwTfuO0Cyp8bvpjGepKZjapdtoHFeIEpzoiKKrOibYwt22uWDIhr/koKTCEBdaWefEXDJ/HVEYdmuTOpUd2ouK++egIJbnYY/TZo60h7ruqdu4aYzFlUqo67nu6BLvNhl3IMWqYg5T5XNiEse14aoDba/1DK8076opwOeyc7Aixrtw3ZmX2lcYBNF3SHEjQGU6xfpplAF5rChKIZQjEMuxZXUK5z01zIMEjxzsBY1UyN6TOabcNqcc6pxE7r87QD/rYqW6CCYUznRF+845NC1JsfaHJaNZSj8Xsma7Rs5f8IWl3zklrxkHTJV890IyqS95+9aox3jubEBQVullX6SUYV3jomtVsri7io7duQNMlW6rHDnBv219PLKNS5HFw1er5VxATQrC+wktTf4IbN1SMCAsKxDOGx7jCO2cTHk2XZFSNQpeDajOPYSCeYUdtfs+aEGJoMjweBxsCHGoJsrnKz/27a3nz3lVsrfZztC3EM+d6qS5ykdU0VE3H73Hyrw/t5VhbiHdcPbI6+47aIpoDCfPcF4+oezOITQicDhuqouFyCBr745QUOCmfRRhcrpStlJKfnTSKgJaYFbCritxDE+KMOXObqbzptpoimvoTXOyJ4bCLIXlfVdNpGUhQ6fdQXODEabfxzmvq6Qyl2FLtx+WwsaXaT39sWGVuW00Rf/PADoIJhes2jK0q7nbYuXlTBfGMissuuHt7FfvXllHgspNUVLbW+GkPJqkqcg+FEbodNlwOQZXPjWuUvO/rd9Tw85NG3aY35pk03ruzmqvXllBa6JpSwchcsppO60CCqiIPRVMsgjsXeN0Obshz7vLhtNuwCyMP4ME9q9id4zTpjaYYiCu885rVhFNZKrwj+2Mio1Losk96HT+wu46Gvjj1ZYWc6YxwvC3MrlVFS9rgAWMC+s5r6rlqdfEIY3B9pZ+yAgfRjMrOurmpXH+8LcQLl/pZU1bIW/aumlJfq/S78XscxDMqa8stidvF4o17avnygRZ0DAdkf857LofNvEYYV5Z/X30pf3rfdmIZlRvHuW6rijxDYapVRR7WV3ipKXazpXps/7t7RzXfPdTGpiofa2awErK+0ktnOEWFz0WR6RRLZFQzhE+SVEYaedVFHt62bzXRdHbSwtuD+UYFDsEN66Y2Ro3GY36H22Gfs5y6pcCgU80u4NY8te4sLKbLdI2eD2DU5bkGSAJPYRQZnVcyqk7cjN1t7E+MMXpcDhvvuHoVLzf0I4DWgSTAmJCiXAKxNJf74hQ67Uwe8DI3PLinjoSi4XUNT6SDCYX/fa0NTZfcsKF8aPVkNmQ1ne8dbicQy3DL5gquXVfGe66b3UoJwLnuKFIaQgz3qNV4nHb2rill9+oSpA6nOsO4HXb+55VWoimFEnNS3J9QKM6ZHBYXOrn/qlq+9lITL1wMIKXgls0j816cdhvvvraernCa7kiKn53owmET/MpN62a1EjNIUtFo6jdU0m02+NhtG/A4DRGBnkiaHx/rAODtV6+eNPF9PO7ZUc26ci/VRW4KzN/8qXO9XOiJ4XHa+fAt63A77FT43CNymu7fPdbQmEzKdFOVjy++ECeaUhmIK8QzGuU+Jy9dHqDc5+LXb984Iq58MHQtmdWQo/p/hc/N63dUk85qbM4zgRBCDBVTnS5fe6mJVxoHqCny8A9vvWrCVcXFQghjBabA5WBztW/IEOmOpPjTH58mldV489463nv9yDDDp8/1crozwrqKwklzxrxuB3tMx8dtWyq5bYmHtQ0SS6tc7ovTHUnz4VvWD3mUHUKQNftUZo6WegbHm9aBJLGMOqXrvtDl4IM3rSOryaFrzmLhCSfVIZmjSGqkkIFNmHkauhwj5DJIOqtxuS9OKquxrcaf19l1tDXEBXNV70hLiHBKoaE3zqn2CB+5df0IB0K518XVa0qp9LtndLe/dl0ZO2qLhu4RAJU+NwMJBU3T8xbMXlM+NeNK0eTQ/6FUlvIZhGQ+uGcVTYE4q0sLp+2IWsoMhhpLIJ4eK4hhYTFdpjvj+B7QBLwZ+DCG3PR/z3WjRuN22qgwa2eMFwPscthoC6ZoG0hyqHmMAvYYnrvYRyarE01nOXB59nLV0XSWV5sG6Aqnxt1GCIHP7RgxGCcVdSimd9Cwm3VbUllOd4Q53RkeknkMJ9I09E0sh9k2kOS1poExXqtB9q0ppcBlZ299yYgJtM0mSGY11lf46AyniKaypLM6h5qDhJNZDjWPrdB+uTfGq80DnOwIc2KcCu4lhS521BUNVWVWdUk6OzcTqkKXnR11RRS47OyrL8XrdmC3CZr6Y5zpDKOoOoqq0xFKzngfmazO6c4wDX3DEqkx8zfOqBpZLf/tV9cl7cEkiWn0h+5IinhGJZhQuNQb43x3lFMdRljbyfYwl3tHhqlJwGE3FA8z6sjk2TNdEZ4628NzF/t4Oc+1oemSjlByRr/Fua4o6axO60CSaGrsTayxP87J9vCC5bvlQ9UkWU0STWVJ5RxjVzg19LwpMLaWclPA+J1bAslFbf98opmTkFRWQ9WH+82F3hjxjIYmoTkw82sml331xnizrcZP0agVgd5omlebBogkx/Yhh91mGTyLzIm2EIOXwEBcGfFeUtEocBrOnngm/xjS1J8wwnFjGU6NI1XstBmCIOGkgtNuhKAdbgnyStMAoy+/pkCCgViajmB8xvdZr9tBMKHwatMAA/EM3dE0VX43tSUFdEVmLkCrm43VJFzqmZksc4HLzs664jlxCC4lcnMxz/VYct4Ws2e6Kz21Usq/z3n+e0KIhrlsUD7sQvCea+sBxpW3jKUU2oLGzTadjZDVdL7+UjNZTedDN68fU9xO0w2DxyagrHD2A8UPj7Rzsj1Chc/Fx+/bPq5aSzqr8fT5XnQJd2+vYnVpIbdvrSSSzHL9hrkRU0hnNZ4530tS0VBUndu2VPC2z79MOqvzjv2r+Ye37h7zmXhG5asvNRFOZrm2r4SH9q8Zk6C+f20p1UVuEhkVXZe0DCR45EQnV60qxue2c6ojwv1X1dHYHyeYyLC91k9Wk2weFV7Y0BfjsTPdJDIatUUeqidZSbl1SyVup41yr3tEPs5sEEJw786aEa999qmLfO2lZmzCyM0pLXTxjv2Gx15KSWN/gvZgArfDxt41pRQ4Jw5j+tenLvLCpX7cDhv/8d6r2Vjp4+7t1RxrDVFfVkiB085zF/pIZTVu31I5dFP97FMXaQ4k2FDp5c/v3zGlELurVhVT6XOTzGgUuOxU+FzcvLGCTz1xnnAyy8PHO9haMyx37XbYQBphGN5R4W0vXezjfK8xgf/pqQ7ee8PIFY3HT3dxqiNCTZGHD928flqexWvWlfLjo51srzXyx3Jp7I/zD4+eQ1F13nlNPW/et2qcb8lPNJ0lmdFmvDI3iCaNta+MJmnsjaNJqPJ72Ftfyu1bK+kOp3jvdWvIavqIlaqbNlbwWvMAsbTKo6e6uGdH9VA45UrBLuDp8z3csqliRL/0O21DHvT0TBMTRrGjroiNVV5cdtuI60zTJV850ERf1BhjfvOOTXOyP4u5w+8avi4ymiTXVVlXUsDtWyrpj2e4ZVPF2A9jFE/2uu1EUioNfTEyWZ27t1eNuP9f7IlzyXTmXOo2jJmUohHPqGau0HCfefhYG4dbInhdNn7jjs0zPq5vHGymPZhifYWX37h9I419cTRdsnPV2BA2KWXenMAx2+U8LsqjUnclk3tuSgvnV+HV4spgunfkR4QQb5BSPgEghLgOeGHumzWSrKbzhz84iaZL/vD1m9lYOXK1R1F1/upnp4eeJ7Pw/UNtfOGFRqQERdP5P/dsHfGZpkDSUHKScLozzH151MOmw9muKL3RNAOJDJmsNq7Rc647ymVzQllT5OG69WV5c1pmQ+tAglhaRZeSxr4EPzrSQThleNR+fqo7v9GTznK5N46ianSEkoQSKm/aUzdC9OArLzbylQNNFLjs/O6dm3nuQh+nOiI8caabzZV+ommVlxr6EUKQUXX6ImmKC11jaiCFkln8bgebq3ysr/DyujySpbn43A7u3FY9+xOTh8u9MY60higucPLtV1tJZDSEMOK1968toyeapr6skGNtIb7zahsHLvejSagr9vCu6+p573VrRySNZjWdVxoNVcDWoLEaks7q9EXTbKz0UeZ1cfeOavpiaT739CUu9cbYXOXH63Zw+5ZKfnS0gwOXAwTiac53RakrKeDBPasmlTUuKXRxy+ZKdNlPQlFJZTXqywspcDoI6EZh2Fg6S6kZZphUVJJZnYa+BJmsRkGOU+DnZ7qGHh9rDo/Z18sNAzQHEjS647z3+jUUTGNiLxDcsqkCu02QNnPOBukOp0iadYFaBqa3WhCIpfmbn58jkVF53/VruXvH3PSXrx9sYENVCRVeN163g1+5cR2ZrMYXnm8kqWjcv7uWm82J2+rSAuKZIl5pDNLUn+B0R4Trp5hHtFzoiaax9cZpDSb5tVvWU2EWgnzVrJcylzx6qouHj3WysdLL/7tvuCaQqulc7ImhqPq4ifAWi8u3Xm0Z971kRuUHR9oJJhSKPA5u3DjW8CkudPKRWzbw7PleznRFOd8dZUOld0S+zvcOtTG4SP3dw23cv6cOpJHXNdoZdawtYoRIKTrPnO3h7dfOLNz7XFeUpKKRzmpo5iq5putoo1btk4rK9w61E8+o3L+7lo1TFE74waEWbtk88f3wSuWbLzdy146ayTe0sJiA6Ro9bwR+XwjRA2SBeqBNCHEJM4JXSrlljttILG14eyTw3IV+Nlb6UTWdpkCCCp8bXZf0x0YuL7/WHCRkhj682hiAUUZP7pCo6bOPgb1+fRlHbSGqizwThlbUFHlw2ATSfDwfVPjduB2CjApVfhcl3uGfebyFifZgEk3qJDIqPreDzlCS5kBihNHz+OluYmmVREblZHuYwy1B+mMKDjuUFLgQQtAfy9AUSJDOapT73NyyqYKLPXH2rx1exVpV4qEjlMRpt/H+G9ZOS5xAUXWEYM5yQV68HCCayvLk2Z6hPmEXUFzg4Hx3lOvXl9Efy/C/r7VxujNMRtVRVI3+GLQGjBCtXDntUx0RjrYa4Xo3bShD1yVVRR52ry4Zsd8TbWEiSYWOYBKf2zEUE24TUFLopCOURFElX3qhkca+BH/94E68Ljv9sQwHGwcodNm5c9tIz+fmKh8n2sPYbIJQIksoqbBvTQkvNwTIqDr/+tRF/v4thsE7GP6h6pJIKjPC6EnnhJxk88wpq4s8DCQUKrzOaSferyotIBBXqPC5xqxgXbOujBs2lBFMZHnL3uk5IS70xumPZQA40hqcM6MnkdRoG0hwpCWI22Gn0u+mO5LipYZ+UoqO0y64eVMFpzsi/ORYB0JAodOO02GfVBhkOaJqEkWTKJpGJqf+ihBzX1/kF6e76QwlCcQzdEdSrDal2J12G9esLaUznFowBUyL6TE6ZDaXUx0RznVFUXWdJ8725DV6wMirW1Pu5aypJjm6llskNXzPjyTTbKvx09AbY3PVWPW2XNvY4xT88Eg7/fEMd26tZGOVf8r3k2vXldEykGBzlZ/LvTF6o0YbzvdER9Rj646kh3KZGvriUzZ6Uhll8o2uUDLZuQn/t7iyma7R84Z5acUkCAFtwSRSDsd4fuVAIz860klVkZvPv/dqSgs9GNoKBp05uRi98cyY77xufRld4TQOu+D2rbNPIn7LvlXsrS+lptgz4XJ2XUkBH7plPVLKIQnjUCLDY6e72VLt57r1s/cMl3ndrC33EU1n2bW6hA0VfnwuO6ou2ZonMR3gYm+cuuICmvri9MYySOBu3TCCTncaUr+FbjuqJrHZBFfXl/CTY4Zcp6bD9etLUTSo8LnoiaZx2gXlXhd+j2NMrY1nL/RxsDGIwy547HQ39WWFbKjwklQ06koKxo1L7gynePhYB3abjYeuWT0nBU3rSjxc6olRXOCg2DTcNlR6WVXqpcLn5nxPjNZgglebBkims/jdTsK6JJXVcDoEWdPrvMm80aayGq81DRBMKty9vZo/vW87q8sKxoQ5baj08r3D7fgLnBQXOGkbSNATTfHmvXVU+lz87aPniKdVspqkPWTEtj/WPMDBhgEcdsHq0kLWlntH5Ljduc0oonm2K8LGSj8VXjf71pSQymoIJM9fHM7PETn/e90jQyrSkySiVxW5ebUpy7rywmlXN6/0uUFKKvzuMRMTj9M+ZkV2quxdXcymKh/hZJbXz6E3MJGV1Ljs2G02VF2iaDpFBU6kNB0IwlA1/PrBZvqiGQpdDn7/7s3srCvKK1G+3Mm1geM5uX/5ikfOlmAiS2swid/jwJsTamszC8x2hlOsy6PQ9vzFProiKd68exXecdTBLOYXzwTjQqXfRSilkFK0CbcD2Frjp6bYgytPnpYcVEowH//kWCcNfTH64wofuHHtiPuwz+0gklaxCVAkdIRSJDIq//lcI9triyYVrMlqOpd74zywp5akorG2zEsqq1HoCqLqkg0VI42a+tJC1pQVEk1nR6g/TobXtfLGjLmiwDo3FnPAtO4IUsrW+WrIRGQ1SZU5gQiYBsyPj3TQGU7RHU1ztD1kFuQyBAwcNrhmbQnH2o2kwJ01Y+Ntb99SSfNAEhuSS72xWVdGdzvs44osjGZ0nZBPP3mRwy1BCl0OvvSB/bP2EDtsgt31xQRiCttri7hlUwUbKr30xTK885p6MqrG+e4YNaaUNRg5IS9e6qfQ7UDVJEUFTooLXDx9vpem/gQ2IbAJG+U+Fw6bQJNQ7nWSUDQcAt60u46BpMrq0gIa++P0RjN88Ka13JRHZvKps70MxNNoOvz705dYW+GlyONk9+oSvG47H7llw5gJcV8szZNneohnVNwOO52h1KyNnnRWozOYIqmorKvwcsvGChr642ytKTKT9VNsrvLxxOluYyVRQpnPQ4mAlGIIBPzva21IDJWZQpcDuw3KfU56Iil+frKTM51hbt1cydnuKPvqS/nVm9YBRjjauvJC7DZBPKNyrjuKEIJKn4frN1Rw6+bKIW+orsPh5gFOd0Twuu30RDOsKTOSuf0ex1C/ddht3LW9mru2D69ybKzwUul3E0oqeQ1eIUCMChEq8jhIml61fGuWz1/oIxDPcLglSDKj4p3G5P6FS/1c7ovTH1e4bXPlkOTsbPF5nPzdm3eR1fQ5LZYaiKdYo3q5bl0ZmpTEUllWlxbw1qtXEU5m2VdfymvNQcIJha6w0V9qizwr0uABhtS27IIRk8RBSfa5JKFkh3LPQkmF0pzr3e9xsq1m7Dk+2xnh009cIKtJukIp/vjebXPeLovJmcgZklA0aoo86LqcUr8ZzwmWW3RcSsnFnijBhEIiraJq+gij564dVTx6sptyr4t7ttfw+OkeTnSEKS10oqiGjP5ERs8z53s53x3D5bDxoZvXUeCyU+Cyc9Mmowh6lX/kvcjlsPH2/atJZzVebghwuTfOzWZY70S4PTMbuzpCSU51RNhc5curuDkV2oNJLvXG2FFXtCRXqQvcK0eVzmLxWBZusCKPgwqfC03XeeMuQ87X63EiRBq7TVDkcnAgpwq4qsPZrmG1qoY8BRaPtAQ52RZGSkmRx4EmJR+5ZcP8H0weTrWHaQ0kcNltBOPKrAccl8PGifYIPZEUFV4XP0bSFUmTyWo8d9FInP/B4Xbqywr5p3fswWfK537irVfxz09e4NXGAdJZlTKvk5aBBJGUQpnXzS2bygjEMhQXONm5qoh+0wDN6vDN11opLnBhA060R4inVU52RPIaPRU+l1HZHSO/J9UdpcBppy2YZG1pAR+6eT251QZ0XfKjox1Ekll6omlu2VQxRhwBjFXAp8/3EklluWvb5HHR3zjYzA8Od5DVdLxuB2/eW8dDZqy3NOO1PU47R5oH0MxojXKvg1jGZtYPEPRE05R7XTQHEsM3G2mjI5xC1aErnOZke5i15V46gil2rypmW62fHx/twOWwkVV13nb1Kh471YPdJsioGl2RFO+6tp5HjnXw85OdBGJpPv1EjEq/h9piD+++fg3tgQSffvICPpeDD928jvUVXl683E99aSG3banC7bBxsiNsyk576Y06uHptydCxD04XdAmKqgLDnvpcVbZ8az5d4RQ9kTSJjHPa8q9NgQTH2oww0OnewmLpLEdbQ6yv8OatwWK3Cey2uVXtUjUVm5AUuuwcagnyckM/dpuN915Xj98swJtSsjT0J1BUnbSqTyjs0BFK0jaQNJSW5kBAZaEZnGhqEgbiaYrN5OJsdu7D22qLCmjuT1BQYKfEOzUHR0coSXN/HE1KTnVcmYnPKUWjNZhgQ4Vv3NzS+WYgNr6a2dpyL2vLCumPZbhx48zFe2KZ4dEnpkg0mUXRJFJqMGp0OdUeQZeSSCpLZyjFO6+t5/7dtTx2qhtdSrbXTVxLJ23276ymo5rRJhd7YvzybK+5hRhRe2+Q421hDlzuxyYE5T4XOycpPZBMzyyE6/HT3bQFk5zrivIHd2+eVDwhHz8/1UUmq9PUn+DXblucudBEDETGRuxYWEyXZWH0KKpOdziNLiUXeqPsW1vKh25ex3+/1My6Ci8OJ7SFR8bCtueEt/VFx14sPznWSdqMOz7aFqayaPE8G53hFFkdVF0nEEsCs4tTP9wc4HxXFAn86Fg7vdFKAqZs6NGWIKc6IvRE0lzqjXOxK8qeNSX88mwPABsqC/nxsU7awyk+8o1D1BQVUOJ1sae+FEXVuWFDOVV+N1uri4ZmzgJoCyTIaAlA0hVOIaXk0ZNdbK7ys39tKT6PgwvdMcp9Lu7dVctrzUH6YhmypjywomYJJbN0hVM8erKTvWvKRuQTCQQep53r1pfx0DX1eY97cNAHONxi5NX857OXON0R5S/ftJ3Vpcb3neuKcqk3yosXA6SzqvntjKi1IIQYWjFIZFX8HifprEYgkSWYVHDabGyq8nLzpgrOd8eoLy+kqT/OdevLAC8vXrITUwwJX1U3Vit1qfLXPztNKquze3UxGVWyucqH1+XEYRNkshr//uxloqksPreDo61BUiqg6sQyOqFklkA8w9qGAY61hWgNJFA0ScXJTi72JhiIZ9Ck5P3Xr+PO7ZU8e95YkemNKBS4HOPKqkaSCmVFwzUlwumJTRlpni+Q05ZmPtYaYiCukMioxNLKCFXFrKbzga++Rl88zRffezVbakdeB198oYkjLUGKChz86zv3jllNaQ8miaSMYoBzVZG8M6bTHVE43dlBXzSNxJCH/cqBZvpjGQqcdlaVeAwJctXo+1ltrAEQTio8eqqLgw0DrK/00hZM8u45qJ21mCRzcr9ebZ697P9oTnaEiWU0UtkUSSVLmXfyELqOUJKUqR43WIfrSkJKyYe+cYjWQJK9a0r4wvv3L0o7esev3EA4oXCmK0pKUTnZHh2TuP+5py9w4NIAf/XAdvasmbpRlBo0THRJWlHwuIbv6Z2mE0rTdToGEpzsjNAfy3Dvzmo2VU2+MnLX9iqOt4WpKykYKqocSSkcaw0hYajm1mgG4hnOdxtO1/t2TS7xf3SGgiDnu6Oc64pSVeQZIxw0VbwuB5msQqF7acq9X+i+8q5ni7lnWRg9oWSWbMKYtP/oaAcbKnycaAuRVDR6ImmSqbETr3iOxySZJ0chlBp+P5bKsrFiaomG88FgWyUwkJx9st7F7uiQB17RoDuSawAqrC4rYEjRU0h+dLSd/3yuEU2XxFOZIU9WazBNVziNz+OgpsjDhkofTrugN2oYoFtrizjbGcHndlJf5qUrksaO4XFPpFV6Yxk+9/Qlbt9ayb76Uk53RrAJwfqKQqqL3QwkMqiasdowmPeayuqc7AjTEUrz26/bhM0msNkE79i/mrZgYsKl+wqfi0KX3VAuKysgmsryuacb0HXJxd4Yz//f13G0Jch3XmvD77HjdtqpKfbQFTYK1X78J6e5fUslm6t97F9riBCoumRNqQ+/x46q6/RE0mSyOna7IJLMklJ0o4BiIEGV381jp7spLXCwrdbPiXZDMchpt7G1xkckleWFSwGkhP5Yhnt21CCEIKGoeN0OznSGOd8TI5PVsNkEqVEqApqEQFzhXFcUv8cIQ9R0SX80Q3swaUgo2wRtoTjffS3JoWbjhuxzO6gtdHPXtioG4pkxwhEDyQzrp9G/+mMp4opGVtVwjLrD6rrkQEOAZEblti1jw9dCCaN/JRWNaFqjOseu+bOfnORVs8bWe792iCN/cc+o/RpGWzyjkVS0EUZPXzTN559vIKVovGlP3YgQv9lyujNMQtEQGLkKNgTnuw1hFYfNcFZomtFXNE0jEMuwYVTi8smOCD2RDOFUllAiy4aKpVeUdbrEcxKLe8PxCbacGf2ms0rV4UJ3ZMhpMRGdweGJUTh55SWFp7M6J9vDZFWdgw1zb4jOBSc7QnQEk0gkL1zq47fvHJYcP9Q0wL8/04gu4f1fO8zpv713yt+bO1pKOXJcctoFqayxTVrT+M6rLYRTKmlF44/unTyP0O9xjikgnNV0QkkFTUpztXwslX43m6t9OGwC3xTyy3pnOK9PKsZYrKg6qg6uGQwv79i/mo5QijVlUyuqutBE5qZEn8UVzrIwesipGR+MZ3j+Yj8PH+sgktFxBhIEEmPdSkqOt1Wb5GJRNWad8KpqOq3BJLXFnmnV5kgpGrllLULx2d+oR1fADsSGV7o0DKW5x8/0UO51sa7Cx9MX+oins6QUjcwo6c2sDtGUSttAgnfur+e/nrtMRtXxeRxUeN1U+NwUuOy4nYJz3VGUrEbGHHgH4gpZVefx091ouuR0ZwRdl5zqcGAXNnwuO6msOlScDQbtMIHf46ArkiKd1dhY6aPS76bSP3G4it/j5IM3r0NRdfweJ6msht387u5wim+83MRXDrQwEE9T4ffwlr113LV9E//85AVUTdI8kKC0w0Vjf5wtVX7+6YkLHG0Noeg6qaxOaaGLcCKDBuiaJBjPkMgY57rQ5eB4u1GItKzQxQ0byoimszT2GatfoaRKkceBlMaNN5XVKPe5qC5ys7e+hIs9MVqDKdN7buQHJZX8IUOd4QSaLkmpOg4BZ7qjKJpuKLJpkpcuBdAxjGm7HaIpBwh4/mI/J9ojvG5U6F8yMb2wgbBpmCs69EaSbPAMWy6N/XGOmep1BS47d4yWIxdiKLTR4xx5Z+7LCYlJ5ikg+Ks3ruMnxzvZWVc0pl5TRzhp/FaqRl2JZ06NnkxWQ0rQMdTuQkl1aDxSdXA77UO1fUIplafO9bJnTcmQOl1PJE1/LM357iilhU7esKuaa9ctfynrjDI8sOY6VuaM4Tx1PKOU/lKKEQa6pqxwhPJWIDE89imjxrIrBU3XUeXInJelRIXfjSYNJ0HhKHGCQCw9pCw5m0LUiUyGUv/wGJG7It0ZSnGxJ4aqSV681DcloycXTZdmIesEfbE0UkJjX/7+X+Z1kcxoOO2C0imEs870iL0uB1ld4nHaxjiipvwdbseU85ItLJYry8ToGSaSznKsLUg0Yy5lS3juQveY7ZScGHNVQlN/fIz3dZAKr23EytCpjjCvNA6wqco35cnTfzzbwKtNA9QUe/iXh/ZMOaZ2UPJyEH0S5aypcO36cnihZeh5X2LkBPJQ8wDxjEZaSdEbTVHtc5HIaGTHCVXSJJztjvLoqU76YoZR9r1Dbfz26zbhtNso9Tp59kI/vaZnNmeuQkrV0HVJU3+CYo+TUFLBaRe0h5JGaFNO2xw2Ixn/zfvq8Htc/OhoB1LCbVsq2b+2FCklx1rDtIeS9MXSrC338vod1UOyyU2BOH3RDNevN0IiXA4butmWAqfge4fb6QwbBnI4kaHWFABYV+7laGsImw0ONgZwO2ysLSvg5ye7jHpHGFLSTpsYunkKAY2BBKGkQlc4iaJq9IRTpLM6PdE0jf0xeqMZVAnRtIrXZeNyb3wo0rzK7+bZcz2U+9147HZjoqLpOOyCQpeDO7dW8ZNjnWNugj63jb6YQnZwQicEKUVnsLdrjFzF1FQjZyfSHaPK52LvmlJ6IiOdBMo0M3M0OaiYxBjZ6UKXg8PNAyQVjf05OURD+zKvSykhpYzslxurinjhkrHSk69g8PZaP/erNXmv43haJRhXUHWd3jyx36c7wnz++UauXlPCR2/dMC2p7VzbM6NJRjctlMwOrVTaBLx4uR9/gZO3719NbZGHnxzv4HBzkEu9MbxuB32xzISy9nNJXzRNTzTN1hr/lIrcTodMzu8XjM19Tk+uzaLkrHpKKfnNbx+hdSDJtevL+fQ7huuO5U5uR9dOyWo6h5qDOGyCa9eVTauo7nLBYRPDcvR5wiyXAsm0hk0Y94lIUuFUR5irVhUjhKC+fHiVwSZmbrTFRjlNcvvFQDxt5v5AVyTNibYQe6dYK+9LLzTyWnOQWzdXEEykCcQNI7s9mH+lsz+WMUWWoD+mUDNPAgFrywsIJRTWlBeiS4lt2hmTcLQ1yJnOKHvrS8YN17NYXA5c7qcjlBq3qK/F5CwLoyepaAwGNgQTKi0DyRHTtAvtY+NglVHj5aOnuvmN2zfmTezsTehEUgpSSoQQHG01QudOdUS4aWPFlCYog2IJvZH0iCKQ+ZBScqI9TFaTXDWqkrNvigm7oznWFqI3kubu7VXsW1NGbbGHnmiafM6+lqBhaKkSnj3Xwy/P941r8AwSTCg8e64753mWN+6uo7LIg8dh58dH24ePz/xfAFI35EFjGZW6Eg82BLqUZFXJ6F9iV10xW2v8XLW6lJaBxFDbBz1+Pz3RyWd+eZFEWuP2rRUkMhpXrynB7bQTSij85cNnUHXJfbtq+ODN6/G5HWSdNlJZHU0KusIjJ/s/PdrON9Mq8YxGLJNF6mYopIR/f+bykMEDRghe7iqYLqE7kuFbB1uIZozPpFXdVHKDjmCKsGl86BJebggQTWtIwIZxs9V0oD/JoaYQ6yoLSauG4ZPVBHFFZXWph9bQSKM4lhk5kdF0icMG+hTmNwcbBpDA7aNWX8KJ6YVU5naVnliKVeXDRsirzQFOdUbRdZ3HTnVz25aR+1JM60DmPB6kxj+cs+HLo9j0/358moONAUoLXXzvYzfgywlvk1Kim21T5diT8Sc/OkXLQIKXGwLcvLmcHbUlY7YZvP4nI5mzkCqAcM4LmmascDX0xWjuj1Nb5OFQU5DTnYaohJRGcddddcWc646ypdrPrlG1ZqZayX3SdipGEcisJmkLJnlglgWYR3OxN8LrMfLreuc5x7hlIAIYUuSxdJZz3VGyms7RlpFjf3HB8C1t9Ok70R7mkBk+6fM4Jk0qX44oqjYcKqzO3GiYT/riKRKmJ+FyX4Jnzvdhtwl21hXz/IXeoe0ys/D/pUeNk2l15Ng9uOI+EFf4ne8e40/fuJ37r5r8+nj+Yj+6lDx3oY8LXeGh1x871c2nH9o3Zvvd9SX0xzNmiPP8raJc7o3TEkyQVrUZ5/Q8cryT3miG1mDCMnqWIKGEwhEzV/lg49IMXV0OLAujRx01Ie8dJUzQFJp8cHfZbRMmN3/95WaiaZV3X7uG7bVFvNI4wIZK75gQnPF45zX1/PREJ3vqSyY0eAAu9cZ5/mI/MLZYaG9kggzQcTjfFeEvHzlDVtVpDiR4aP8qEmklr8EzmuNtYZoCkwcSqzr05oQf6Rie07JCF1tr/COOw2WozJLVGTKmQokspQVOyrwuMlmdhKIyutbY/bvr8HscpMyQtju2VpJSNK5ZV8aLl/r5/PMN9EaMGkKXeuNcs66MHxwx1Ndi6QwXe6L43I7hFaecRsUzGuU+J8JcO4krOofaIuMebyiZZTI7QgK98eyY1wsdw6shg8Qy2pAxKBlppGgYdaWEsKHpEEsbKnuaDm67cR7Hs0ltwhAWsCEnba8qoT2Yoj04MhSjuTMAbJzk0/kZiCaA4Vj3021h4qaX9VhrcMz2ufOY0KiwuvPd0aHHHQNj++SFnijJjEZWS9MVSbMlx+gpdDmNcyElJXnkosOpLIqqo+mSRGpkx8uoGj862kEoofAGUx1yqoz+WTQM764QhhKfomr0RNNm24z8qtUlBbx4uZ9gXKF1IMmWav+QMyad1fjeoTYiKZX7rqoZUYF+Ig41B7nYE+XqtaVDk3ldMqQ6mE9cYbaExxHGmA+OtIb5sPm40OUgnlZJZvUxRSV7cto0es5f6LKTUTUEAu80QpCXE8shj+nVhmFDNa5onO+OcqcZclvmn5t6Txmp8fS5XhKKOiac90hzcFi9EkPo6GfHO6dk9OxbU8KpjjBXrynlwOXhiWciXxVnjOv9zXtXzfQwpoyiGeHXSEM0xz0Df0lXOEVHKLUiV0BXAj6PgzKvi2BCGVo9tJg+K2LkHzvtHEtPNDXhgm80rXGqPUyFz83NmypwOWx4nPYph8HcvrVqjAd9PHJrGLjsNmxieFJbVjj9lZ5fnuuhPZjE67ZzsSfGm/7thaHwv8nojCQp8TiJZyZ31aZGbfLI8U5T/rptxOTP4wCEnWyOq04CTYEkPdEMVX43qqYzKrqJloEEFT43x9tC3Lq5kn1myEE4qfDshT7iGQ1p5ru8ff8qbthQwc9PdgHw1Ll+I2RP1XnHfuPm5bAJkrqRayGASq9zKBxhMmajwKtLxqyc5T7Nd3s0zoU+lO+SHucmmm9fSDmlADWJ4SUvHVVIMqTM3E1/vDnE63evG3p+tG3Y0GkNTpznkZtrBvDoiZ6hx/E8x69LSTSTpUC3U+Ebadhc7IkSSijoEg7nMbbqij0E4xk8TjvVo0JM+qKZIYXHCz3RMZ+dLqFEhrJCM5TTYcfjNIxZl9NOqdfFNetLeaVhgOPtYeqKC8hdfO6LZgwZd7MO1FSMHlXTedlMWj/YMDBk9PhMGfaucIrd8+C5HUgunITs5Z7h37Q/mhpS6gqMyoHMrd3lso8cu+02wcn2ME6HDYdteSvnjUdnHmfBUuNQU/+I51VF7iHH5pE58mCH4lku9BkOxCJPaMR7o8t4uewCIYzQoVs2VdAVSdMRTLIjT4HhD9ywloMNPm7dUsHnnrk8J22dC7ZU+2kO9LCh3oh8mAmJjEoyq5FMT+0eabGwOO023nf9GhKKNm7tKovJWRFGz1T48dFOfu91myjzj1+ALBDPUFfi4RsHmwhE0yiqxm2bK+c8/n5dhZe3Xb0KRdXZXO3nxg2lHGoJ4XXauWP72Lo2E5FRdT77dANg5I5oUqcnMfW4gAu9KXbU+BHmCsqE+xr1/GBDwMyjUUasMkQVyJeSaUzmNbK6EYKRuz+BcdO5a3s1tWaRuNMdEZKKytZqHy0DCYrcDnwVXm7YWMFd22qw2wQ76oqIp1WkNBR6hBDE08a+VV1SUuAgkMgiJZzvnYdk6zzoEjJTNDqHPmP+X+AQ0wpLmW4ASyqrsaFipApWU8/YOlZTpaJk5PXUFx/uJZPlIdvlSHfkZLfaaFrFbXr2u0JpyrzD++6PZYZyQOJ54mI2V/uJpLJ43Y4xqwPVRR5WlxYQTChcNSrULB+5OWv5SGYlvdE0rzaFuP+qVeypLyEcN1SenHZBKKHQHkzROpBASjlCbamuxEOBy86J9jC6lPRE0hMWTQQjD25NWSFtweQImXcwxpp1FZOrns2EskmEReaSRE5ilctuG1FnKpeHrlvDY2d6UDSd+3aNzMf87wPNHGsLAYLvH2njj16/8gqXtvUt/bCXzCiPks/toL7UcESEUnOzeqgoGaMOmqaPuX4udI7Mv9m5qoR15V6OtISoLfbw0xNdRFJZWgYSvOvakcbx3/7sLF2RNM/n1AVcCpzqiJDKajT0xclktRkZPhnViPlLqXO/KmwxNxxuCdERSnKTldMzY1as0bOm2EFbZHgpIZVVSU0iEvDmfXVc6onwyHEjd6WpL8Yf3L1l6P3vHWrlYMMAv/26TWytnbiY2WTkFlf8x7fu4acnO9lU5WdNeX6xhfHojaTIXeh89FTPuNuORywz3VR2g4NNYz3qk+F22HDZ7bjsYkSOjM8JVX4P9+6sZk2ZlyfPdPOdV9vQkVR5XUSTCnab4P6r6ri6vpg/+dFJqorc/O7rNhNMZrlxYzmvNA5QXODg+Uv9rKvwGbHNYvzQsPlimvbOCOY7Dr/AYaeyaORktaF3nI2nQCIxcpJSXmCnNzo1T+HPTrXw4LX5ay7lY3Wxh65QiuIC5xhZ1ZJcz1eenJ5QPE1DfwK/U1BSMHLYczls49Z+ysdUfiG73Ugl7o+leeZcL33mqlY0o6JpkrNdEVJZnXhGJZlRcTmM1TeH3cbuVcWkTGW0QDwzqdED8NZ9q0hmNXymRHgoofDMhT78Hgd3b68eN7RX1XQiKaMGTr5V7VMdYU53RtizumRM7tH9e6d+zmZLb45dLidYs796TRl/8+AueqNp3jnqN20PJVFUiUDSNjD9MOLlwA+fbl7sJkxKaFQE3pdfaGJ/fQn37KrlyOWJHTAXuyP85HgXv3Lj2gm3iyYyfPCmrSiqTumo+k7RUcPDmrJCwimVNYUuvC47z5zvJRBXCCczY4yeV5oHSGTUIUGcpcKRliDJrE5POM2U4trz4HYKsqpG4RRD+i0WlnBS4aWGfjJZfckqMy4HVqzRo4uRF66qg2+SOO4Ch53vvNI69LwrphJJZijxemjsjfHnD59Bk/Bac5DX/vzuMZ/PamNjzKdCQlGxIUgr2pAc5lRJKiqzje5sDy1MmIrLDltq/ATjOepjJkkV3nPdGtaUefn88w386Eg7HWEjF8LjtOF1OSgqcHL/VbXc9+8HhryFZYUuSr1uaosLeNOeOuIZ1aieLaCxL05pzFqqz2VvfdEYFa/Z+Ib74iNjFDsjw+d7smF5IDS9lbejrSE0CcFklqb+GHvXDss+H2nJCX+KjxVmePK8EVITy0q+/EIjvz/PXv5ALEOFz0WJx0HA7O8SUFMqZ7oixMzVKDVj1NfIve73rikhnMrimkbys80mhgwegMMtwaHcrU1VPjbmUbyTUvL9I+30RTPsrCvi9TtrxmzzwsV+VF3ywqX+MUZPLLVwhTNy9/TU6dZxt8tqOoqmm/WvRravpMA5FD5aUzw/q1SBWIaSQuesRShmymtLay4+hts+9csxr+nA737vOBf+oZaJRgRN07j3314C4EsvNk24n4Gosao7FV2gogIHV60q4YYNZaQVjXNmMdFfnO6B94zcNpkxohRieULADrcEuXbd1IupziVJ836oSuiMxNlQWTLt7zjSHEKVEErMrECqxfzisNlo6I0TTk2tULNFflasSR+IjU3oPNttTIxSSv6b9fdfa6FslBf4WEuApKJyrGVgKHym1/TaavpwRfqHj3Xwri8e5A+/f5xgnronGVWjK5zKW8H+bFcUVTcUlkLTTESdTe7JQvOWvUZIXzqrjU26F/DTE+3c97nn+dLzDQTNMCldGtKzKUWlqT/Be7/8yojwiL5IcuiclpsiCZqUOGyCtLVMP4anLw5womXuQmD6IiNX+wZDC6dCR3B6qnG50tFHWkfemNWc4oCZSeLqOsJjp1aZrDZGWGE2ZHX4yoEm/umJc0OqfmBM8BKjct2+/Wozf//oWT72zcN88vFzZFWddeVGWNqgE0XT5ZBARD5eaQzw2acucbbLEOeo8ntoN8eTCl/+mV9G1YdymcbzXK8xJYTzFSxs6p36Sm9HKMn5rghyDjyUPzzQMO57PZE0jf1xY3/dI/Oznjg7vKT5PwfnfkXkUk+Ma/7xabb/xS/Qlqhc9GJyvq2PtnB+J1RalSj57gs5XOqeehhuVg1PedtLvTE+88R5/s/3T/DTY8MqpPnurYMCNfnu4//487M0B+a+SO90udQWmnyjPAwGGcwmUsFi/lB1nc3VPvasLqbcZxk9M2XFrvTkm3s9f76PmzbV8lJDIG9cfktYGWMFfv2VFk51J/nRKyOTFn9ytJ2fnuiiwu/mj1+/lb985DRxRedYe4T+WJovfeBaCt0OIqksbrvgh0c7CMQVNlX5eNOekSoxu1cX0x/LUOlzcaI9TJXfze7VJbM9BUuOj79xB//+zGUONQXoG5WArOnw4uWxkyi7TbC9toiTHREk0DPKmHU4HJT7XNy9vZpjbSGiZr2l1CwK2610fnC0Y86+65eNo+phTOOz/bO4ub58voeP3jYcenqybdgImuxr9466toKJDB//8WmiqSzvv2HisJnpoOrwncNdY14fPe78/GQX4bRGbzSD09bPQFwhldWx2+B379zMhkof3zvcRl80w3Xry7h5VDy3our8zc/OEk5meeJMF7dtqcLjtKFoOsmMyhNnuikya2S1BhK8cXcde+pL8DjtuB2Cx0/3sKXaT3ckRe0okYc37a4jllbx5yne/NknW/nw63ZNeh6ONAd4z5dfQ5XwwZvX8tdvmvwzE3EsPP57utQ5cClAKquyqmTkseSe9/T07O0pkdGGa8d94bnz/M7dO+d+J8uYf/zp4XHfcwr404dPj3m9P5bhF2e6KXQ56O9tz/PJ/Hz+UIL/+7apbXvw8gAa0BHJINWJc4oGbfZ8tvuJzihPnu7mN163ecrtnA9aQ0tfzMJi+pQUurhlcyUdoRQ3bSznnxe7QcuURTF6hBB24MvAduBZKeVfLMR+XVISSWV56mz3uKE3oydMBxoinO1KMlqI6o9/eGpo22qfi3iOG/p8V4hQUuGVpgH++6Vm/B4Ha8u9eJx2+mNjvcnbaorYVlPEE2d6ON0x7KmdSiz/cqHYBd3hBL2RFOd6pu4N03RJkceG3Qa5Czfban20DyTpi2YIxjOc7ghT7HFSU+xhR23RuN5tC6gpWv7n5kjzSK9vzzTCemLpkYbz5d44gXgGTZdmovv8MnrsCUQSBNJGMU5Vh+cvGXLlBU47h1qCnO2KcqQlRH1pAc2BBAVOOx2hJDdvquBiT4z2YJyeaJpkRqMvliGc7ESTxuqVTQi6oynqSwo53h4mllZ58lwP9+2q48aNZbzUMIAmJYF4hrOdUQQCRdWHVnhsNkHxOJXkp+p3/9g3XmNQjO/rL7eOMXpaA3G8HgcVvtmPd52hlFH/SQrag1PvFMfbgvzns43sXFXEH9y1ZcqyvcfaQvRFM9y4oXzE6/9zoMUyekbxUuf472UlPH5y7AZv/8LLaLqkrNBFuGvqyooSWPfxx2j51P2TbpvrqHm1bewqsKLq2ISRb+d2CNLq+OUBvvrCpSkZPRlVI5LKUjWBsNJMGQjP/xhmsThct66MvfU6nhkq9Fks3krPfUCHlPIjQohfCiFWSSknGBLnhv882MkXD3YyXSdfNE/8bu6g9/WXR4ZKKBmJz+Pke4faONURxuO0U19ayOrSAq5bP37M72BMvsMmKFhhnTqiwP3/cXBGn32xIcS2ai8Xeoc9WO2BBLqEs91R2kJJ4hmV+tJCbthQzhvy5CZYDPP1l5Z+svNkzFxvDv75l438xp3DOT2rSzy0h5KkFI3r15VP8Mn5oXfU3DwYN2pEhcjy8uV+/AUuOkNJOkNJ3nVtPX//6FliaZVnLvRS4HTQ1B9H03SkKV3eF1OGUv2FgIaeOM39CWJpFVXTcTrsPHGmmwOX+wyjKJzG5bAhpeR7h9uQEu7eXs1Vq+emeGdwgqjBD33tIM9dNiZp//7QLh7cP7uVtlXmOBvPqOyom3oxyLd+/hUAnrnQx95aH3fumry2SnNvjLd93hjTdlSPDP/rXzg17xVDvhSxNtNw7QzPTNXtnV98aVaiFe3BJI8c78TpsPGua+p5z/X1fOflNsYLQg+YzYwkFX7nf48TTmX5k3u3cuuWYVXWaErhxk8+Qyqrc9f2uVfhevnczGT37cJwvFg6BksTVdP5g+8e53xPlN+5c3FXE5czi2X03Aw8Zj5+HtgLzLvRA0zb4IHhAn/jMXqwvqrWTTih8NR5Q9YyltF45Hgbf/T67SNU23KRUnLtulJqSzwUFzjH9a5eqeQaPGAUg7MBPrcgYhaebArEKSpw8vzFPt569erFaegyIJS5spVfRs+tjraGiCSzaLrkcOviJ/HmDje/PNfLjtoiLvfFEcDJ1hCDP18gnsFuE2hSYrcxZOgMJusPPuk3Q0nLfQ7SGDVJAgmFjOpAUXXsNsGeVcV0hJO0BpLE0grtwQRryr28aU8d4WSWgXiGGzbOvUE4aPAA/N4Pz8za6FldWshbr15NKJHhzm3Vk38gDx/+9glaPjW50fO2/3px6PG5BZLDt5geh1rGL0A9FT7zxHmeONuL3S7YVePnd163hW+/3Dbp5x4/002Tmd/zndfaRhg9//rkhSEJ9qfPzb3E+Ey74mDO8mLnCZ/vjtDQl+DObdMr37HSea2pj8fO9CCBv/7ZGaysnpmxWEZPOTB4t4sCY1yKQoiPAR8DsBctbuef7hTx5Y4Mt3/m+RGv9Sc0Pv7wGS50RfjLN1/Fv/7yAj882sk1a0t4cM8qvvhCI9G0ysdu28C7r1uDlJKLvTEcNkFTf4KWgQR7Vpdw/YZysppOVtMpXKFVxaeKjuGVUnUQEhRV0tQfJ7gMqpJbLB1aA0kU847fNklB1YVG1eFMVzSv7Ho4peK0GTLwCUWfNGQ3mFAp9TpJZDQyWZ2UooI05LVfaRxAIDjVGaY7nMLjsrOmLMGJthBOh51tNf6hApJLma5QnD/+4UkAnjvfyf/++m3ztq/R0ssW43O6M0LtYjdiBjx+pscwAjTJ+79+mLICB34PBCdYeHr7518mmMiQ0TQKHA5uGuUsuHZdKd941chP8szDsspiVmo61xnhwOV+3rZ/NZUzCN3rj6X57e8cI5RUePnyzJwWK5WOcHpojI9nNCbTCVz38cfGfW8qYZ8rlcWaNceAwdiDImBMaWMp5Zcx8n5w125e+nfbKfI/r7WzvtrP115uQVF1fnG2lzMdIXrjhgzhY6e7efd1azjWFubFS/08dqqL3liGVcUFpLM6O+qK+O6hNlKKzn1XWWFcAykVj314Ba8/rvD9Q218+Ob1i9oui+VDmd8xJGziWiSp4YmYzNaYqjGiS6PAa3ZQpkkCAgSSUq+LUCo7LLsvIZLKsr68kJ6YQlJRx1QBv3cJLqY+8B8vDT0+2DybIEgLC0NIJ5tzfQVTKnduKebZS+OvIB1rC1PoslNU4OQnv3XTmMn//XvrudSb4OWmAT799t1s/+q8NX9BiSYV3vmlV0hlNb79aisHPn7XtL/jSPMATQHD8fTIye65buKyZnWpFxuGE6vIs7LSHxaSxbrDHwUGr4ibgPFlXVYYTmHECQ9OrgRGnQCn3YbDZuPeHYZ3I6vp6LpGbyyDDeiJptlS7ac3miGR0dClpCVgqbQAY2oj6VLmlRS1sAB4YNfIOPoybwFFHjtep43N1VPPA1koBMZAbR+VW+9yCNZVeFlb7qW4wIFNDIe42WBMCU+vE8oLnfg9dooKHFT43VT6PWys8rOnvpjN1T4e3LOKvWtKeMf+1fzdgzvZWOXn3h3VvOe6Ndy0sXxEvP+XfmfpeQurJpByXVMyPFG4e/PsiktbLC2qfDbu2DKx7/uBHdMLz3xoXw3v2F/PtpqRNa58bheNn3jjuJ+zCSOXrsq8vvLxf+7dxo9+82Y2VM39eHP44zNb3RwcL2Y6nW4dSJAy5fn74zNbBg2ncj5n3cNHcNPGCu7ZWc368kL+7I2WSMpMWayVnh8D3xFCHAB+IKWcesGHOWR1sYOOyPSzfIo8dmxICp02IimNO7ZVsLbSz4+OdBJJZMjqI+PyP/mWHWRUnR8f66TU5+a3X7eZN+6q4YsvNrN/bQnBRJaP1vm5b1ftUOHIa9aWYrcJzvfEaQ4kuHljBW/YVUNWM7TaY2mVq9eWztGZWFqUFdqIpvShugEAPpcdTdNG5E/ZBGyq9HH39koePtFFTySDz2Xjk2/fjcNuwzn1Gq9XFN//2LXc9j/Dz2ez1H3mL+6Y8We/8vaR0u1Xl8OxKabUHPn47SOeH/rT13HdJ58D4L3X1I3Z/g3by3ni/AAFDvjcu68d8d5tWyq5YWMFfdEMH7llPd+fxjFMhs8J8RwdlDs2luJ0OYfy/QC+8N59fOu1VnavKubnJzrojGYpdsGHbtnIpb44XZE0d22vIpbSCCbSFBe48Lrt3LSpgm01RcQzWR491U3bQApd19leW0RS0bhlUzlZXfLNg60IG3zg+jU8fKILu83Gh25ei6JJBuIKq0oKKCpw4rLbKHCNnPLcuqVqxPNttcUcydNfGv7xPjb9+S9mda4m0hQ89WfDE7mWT90/bujGs39yN/v+9gmiKY2vfGDviPde/PgbaOqL4XHZqSsZv6TzsT+7fdz3crn49/ey9S+fBKAEyPVL757SN8w9E52bheTc393Ljr8yzs1Hb6zl73PGm//+wFV8+Ftj5alzGRyT+qNJrv3EcxNue++OKr7w/mv42cku9tSX880XLhMadVv/p7fu4l3XryXw+Rd4tW189dDSAgdvuqqGv3vbnqHXmgMJ/unRMzxxIUChS/DZd+/HbhM0/uMb2PjnT4z4/D+8ZSerSjx0RzK8affCBfT9wd0b+NpLLdy1uZLKkpkZUl//4DU8fraHt+wZO35OhR11xawvL6ArnOaWLTMTaHhwbz1ffKGZ/niGh65ezaf+e0ZfsyIRQvClD1wz9PzTi9iW5YyYi2Jx840Qoh8Yvwz3SCpY3LDW6TAXbb0aOLYI+50JC73fic7NfLVluXzvTPrNfLVlrpirdo13bpbKcS9mO2bTb8ZjqZzXQWbanvk4N3PBUji/V0K/Gc1U2zd4bpb68SwUuedhOZ2bpTC/WQ7naa7Jd8xrpZR5xQCWhdEzHYQQR6SU10y+5eKzWG290vabj/lqy3L73pmwlNqSy3y3a6kc91Jpx1yx1I5npu1ZascxyFJt12xZ6sc13fYt9eNZKPKdh+VwbpZCG5dCGxaa6R7z0svatbCwsLCwsLCwsLCwmEMso8fCwsLCwsLCwsLCYkWzEo2eLy92A6bBYrX1SttvPuarLcvte2fCUmpLLvPdrqVy3EulHXPFUjuembZnqR3HIEu1XbNlqR/XdNu31I9noch3HpbDuVkKbVwKbVhopnXMKy6nx8LCwsLCwsLCwsLCIpeVuNJjYWFhYWFhYWFhYWExhGX0WFhYWFhYWFhYWFisaCyjx8LCwsLCwsLCwsJiReNY7AbMBiFELbAXKAYGgMNSyvBitmmqCCFKAF1KGV3stljMnOXcBy0mZqn+ttbYYTEeS7XPWozF+q3Gxzo3U8M6T9Nn2a70CCH+DvgGxg9eDlwDfEsI8duL2KxxEUJ8K+fxh4CngJ8LIX5z8VplMRvmqw8KIVzj/c260RZTYimNL9bYsbwQQmxbpP0umT5rMTHWbzUSIUSBEKLGvM9Z52YKXMnnKbe/TPuzy1W9TQjxkpTyljyvvyClvH0x2jQRQohnpZR3mo9fAu4B0sBLUsqb53G/1wJ/B/jNl1SgG/grKeXledzv+6SU3zEfbwb+CdCAv5RSXpiv/S4k89UHhRBHAQGEzf/l4P+DfWihWKz+M4V2zWv/Wkrjy2KNHfPBUutPs+lHQojfAj4GZIHvSyk/Y77+7EJfp+Z+l0yfnWuWWr8ZzXT70Ur+raaDEOJB4M8w7nUxoAi4DnijlPKVUdsumXMjhPgjKeW/mI+vB/4VYxz4cynlywvUhiuuD43TXyTwt6P7y3gs5/C2C0KIv8bweoYwBsO7gL5FbdX47BBCfAWIAyVSyhSAEELM837/A3iHlLJj8AUhRD2Gh+CuedzvR4DvmI8/B/w9Rkf9PLAoEwLAAyi5L2MYEjfN8Gvnqw/+BvCHUsr3zPJ75oLF6j+TMd/9aymNL4s1dswHS60/zaYf/Sqwz3z8aSHE70sp/w1jXFkMllKfnWuWWr8ZzXT70Ur+rabDnwF3SSkTgy8IIf4H+K4Q4r0s3XNzP/Av5uN/BN4HRIGfArcuUBuuxD6Ur7/4gUeBKRl6y9no+RhwH/AAxtJeDDjKcEdcaqzBiLssAr4GIISwA38yz/tNA/Y8r3vneb9lQoibMSZq5VLKVwGEEIsVUvm3wFuklHO59DsvfVBKeVgI8f9m37w5YbH6z2TMd/9aSuPLYo0d88FS60+z6UcRwCWlzAD/VwjxDXP1Z7GOZSn12blmqfWb0Uy3H63k32o6hIH9wIs5r33ZfG0pn5s6IcT7GHZEtQAIIRYydOpK7ENhxvaXq5hGqs6yDW+zmBpCiC0YXqc6jI4hMW7WfyWlPDqP+/11jElaEYCU8i/NidonpJSLMqEXQtw41SVQC4PF6j9TaNeS618Wk7PU+tNs+pEZxmSTUl40nwvg/cBNUkor32oOWWr9ZjTWeDQzhBBVwKcwJrIxQAfOA69KKb++mG2bCCHEvRi/9VogIqX8ivl7/56U8rOL27qVy1z0F8vouUIRQpRLKQcWYb8VUsrAQu/XYm5ZrP4zGVb/Wp4stf5k9aPlwVLrN6Ox+tHECCE+AVyPMXm9KKX8HfP1RcmLmypCiE9i5B4tq3Yvd+aivyxb9TaLqSOEqBVC3CeEeLcQ4h5hSN7+6gLst9LcVy4fnO/9TgUhxFOL3YblwmL1nym0a8n2L4vxWWr9aa76kTWmzC9Lrd+MxhqPZsTtUsq7pJT3YOSo/JP5+lLPV7xtVLs/bb6+1Nu93Jl1f7mijR4hxDohRMM87+MOIcTT87mPSfafV9YQyMzzfj8BfA/4oRDiP3PeeuN87ncaLOd8tgVjsfrPZCyD/jUlhBCrhRDfmKPv+oYQ4v1z8V3zxVLrT3Pcj6wxZZ5Yav1mNCtlPFoEBoQQpQBSyv8ENCHEP2P8xkuZ0e1Wl0m7lzuz7i/WIL3yuXM8WUPgv+Zxv7cPyukKIX5HCPFpKeVSSry24jqnxmL1n8lY6v1rSphqVB+EIRWaP5ZS/vV42wsh7gD+Qkp590K0bx5Yav1pLvuRNabMH0ut34xmRYxHi8BHMFTHQgBSyj8TQtwKLPWQwOXa7uXOrM+7ZfSsfBZL1nBACFEqpQxJKf9TCPEJ0yKvmOf9ThVrGXpqLFVZzKXev2ZCOYb06bhGzwpgqfWnuexH1pgyfyy1fjOalTgezTtSyv48rx0ADixCc6bMcm33cmcuzvsVLWQghFgHPA08DLwd6AHeCXwbOAG8DWN5+m7gtwEX8O9Syn8xPa5/C3QAd2BI6L1XSimFEG9kuFjVy8CmxfLMCkMy8z7gZkbKGv5YSqlM9NlZ7rcSKJBStuW8diuGstE/jf9Ji6XEYvWfKbRr2fYvIcQDwCcwJm4vAzcAu4FzGOpUbcC/Ae8ZrCElhPg1DGnOn2Cu9JhqQZ8F3gS0YoT6fEtK+e2FPaKps9T603LuR1cSS63fjMbqRxYWywPL6IFG4EEp5WOmJ2kdsB64JKX8mBBCYAy0B4EyoAnDe3MT8BhGjHEHcAojqfICcBqjUFIj8FVg7TIOR7GwsJgjzDHnAHCHlLJRGJXmvyul3DTohDEf2zGMn31Syj4hxOMYkr1uho2ejwJvMf8qgGPAnyxlo8fCwsLCwmKxuKKFDEx6pJSPmY+/jZEgCYZHFWlYhQGMkJMvYkw6Ks1tjkkpL0ujQvohDM32G4HXpJQN5me/uzCHYWFhsQx4A4Z3utF8Pma5HkBKqQE/Bh4QQhQBG4FXR212L/BlKaUqpewBnp2nNltYWCxzhBBfEEa9I4QQ6mK3ZzkhhPgzIUSrEOL+xW7LUkQI8aNBgYGljmX0QO7FXwCkzMdxMHT2MSYTrwC/hTFJGYzdzlWOyWJUjPaYjwdxz32TLVYCk6ltLbbyn8W84GHkmDMR3wUeBO4HfiLHLstbY80VykIoj1qsLKSUvymlvDT6dSGEVUxzAoQQXuCPgW1mRJDdlE1/abHbtlSQUr5DShla7HZMBcvogdVCiBvMx7/OWG/pOozEyaeBDUDtJN93BHidKUXrwFCbuGKYigSvEOJ5IcQYJR6LK5MrrD88C7xDCFFnPq/LeS8FFAshHEIIIaV8BdgEvIv8K8avAR8UQtiEEBsxcg8tLMZgTWzHIoT4GyHEXyx2O5YAv7vYDVjiVABBM6IHjPzttwE1i9cki5liGT1G3PxHhRCNwGrgk6PeP4GRm9MOfBgjf2dcpJStGAIHr2DE2J+c4/YuaaSUHVLKDy52OywsliJSylMYuTkvmp76m3Pe68UQNmgBtpovP4ohhHIqz9f9G8bqTivwHxjKVlc81gQ/L9bE9gpCCPGQEOKsEKJJCPE9IURRPueSEOIQYBdCNAghPiqE6DDzmBFGAdifmREHzwghvm2GeD0uhCg2t9knhHhVCHFZCPFVU3BiWSOE+F8hRKN5Tt4IvACsMZ/XYKy8XxFy5OaK8gUhxL+afemgEKLe7EufE0K0CSF2CSFahBCrzc88IIQ4JYRoFkL8g/na+4UQZ8x+8huLelBSSuvP+puTP0xhjCls9zxwy2K3d4bHuA7DCP4C0I2R5/WrwEUM9a2NGLldTwANGEbzzeZn7cC/A83mOXgSeL/53j6MnI3LGOIXNgxVwKcX+5gX4Jwu2/6wAOfmTzCECxa9LcvlD1AXuw3TaOtUxpN3YwjktAKfzflcg/n4PcBxwDvOOHIIo4ZQA/BRDMfdoIjRPcDPzLHmGYy81lbgcaDY3GbMdy72eZuD8y6Av1mJ1xawBUNwqc58/lngM7njbO41MurxYeAa8/Hnzb51B8Yq9F7z9f82z50DY7W53nz9u8BbF/v45+D83Wb+/4B5zoautZxtxry2Ev/M49SA+83nf41RJPh5jHzSwWupBWPRYB3GAsFG871Ksz8+jRGOXWiObVWLdUzL3iq3mD+EEBVCiKdN785TQohyIcRPhRAXTc9PjbmdKoT4NHA+N9ZcCLFKCPGi6TU5JIRYtagHNHesA74MbMZQzrpJSrkVw9D5GPBN4DtSyk0Yk4z/FUK4gQ9hhEhuxpjIXAVghkF+EXhISrkZY/Ly5gU8nnlhtMcnn/cxz2fuEEIcM/vME4NhYGb+0xdMb9sVkUxqxpJ/BPifxW7LXDPVvjHKgziU42aGJn1ZCPGcEKJXCPEH5uu5nuu7ze3+x/Qy/qbpmRxM5vYJITqFEM7FOQtDrGPi8aQZ2IOx+veQMFT+ABBC7MZYOXwQI8d0zDgipbwO0KSUm6SUX8UwrvabX/FWhkMnbwI+I6Vci1G+4f8spbFJCPExs38cE0J8xVxZGO+e9GmzD7QKId5jvvZBIcTDQohXgX80v3aV6bVuF0L8a86+8q2U3C6EOCkMNgkhzpvj+lLjHuARKWWX+fyLwJ1T/Ox3gQeFEIJhgxjguJTyhPn4OxiCT9uAXcBTQogLGP1n3axbv/g4zL7wh8BKmbPMhl45udjXICOEeqRRV+f1GOPXCYzoJz+L2E8so8diIj6A4c1Yi5FX8Fngi+YN+YfAn5rb2THC+LaP+rwEPiyl3IgRB/trC9Lq+adTSnlcShnHkCf/ifn6IQyvxk4p5bcApJRHgC6MCct4alsr7uZhTsy+gOH5Ww/8HPgn4B4p5QaMiddfjfpMGfAt4H1mn3kCY2VskPUY5/fxeT+ARUYIcT2Gt/bfpJTti92euWQmfWMcrsXwxt4G/IMQwjFqgj8oArIP40b9ReD7GAYCGDXYHpVSZllcJhpPVgOdwB8BXwd8DE/EvBiT1PeYfWSq48iym9gKIbYBf46hjnpDThvGuyc9ajqd7mdkyPoN5mt/bj6/HcPw2wbcYxo2W8jTH6WULwCXzO3/HvhjKWWumNFSwQHoOc8Fhrd+Knwf4/zcAByRUibM13OvkQIgae7ngpRym/m3Vkq5rENLhRBvwFgV+x6GeIFVcDj/bw+m2Nco8gn1OIBv5vSTOinloXlo55SwjB6LiTgMvFEI8TEggVEc7rPmze+PGekFyacu1QvcJIT4EsaEf6V4TXKL4WkMq/iNp8o1eNMZT21rxd08GCvNfA2Tex9vBA5LKc/nbPO6nPcfkVLqefrZikNK+ZqUslpK+fnFbss8MJO+kY9HpZQJKeVFIMJwKYHRPC6lTJv9ZlARD0auciwmE40nbow8ryYMw+cswxMxFWMCssV8PtVxZDlObO/EuMf0SqMY6Q/M18e7J6WEEJ/CWNHJve+8IKUcyBlDHpZShsxz8AhGX5xopeT/YhQWLsjxfi81ngHePrjqheFsfGKC7bNCiBIhhJBSdgJRjByw3GvjaiHEBmHk7HwUI1zpIsZK2Y0wlN9TPNcHs8DswrgHHcIShhlktNjXRIqyI4R6zP9fwuiPVeZrt89nYyfDMnosxkVK+RKGJ+x6jJhuD3CdefPbJKV8h7mpJoeVTXL5R+Au4HMYN44rwWuSAZqEEO8DEEJcDZRieAjHU9taiTeP0R6fqXgfR2/DqG3yeZYslh/T6RsqxkoywOgwtHwlA/Ix1G+klMeAcvNmvBdjBXopU4IRDvIzjPO2O+e9DIaK1CeFEDcx8Tiy3Ce2hYw0Dp05/4+4JwkhdmIYdo9jTPhz7zujx5B8JSsm6o8ahiE61ZWTBUdKeQZDTOlFIcRloJixAk25fBU4z7CD6fsYTspcQ+kMxqpaI9AHfN285/8K8A1hCEF9mrHj93Ljh8DNQogmjPu2hSH29evmb1wHfGq8DeVYoZ73mdEunweOmK+9dQHaPC6W0WMxLkKIDZgdHliDMUH4TfO9KiHEjkm+YhfGwHkJYxC9Ungf8DHzAv8v4N1mCE1eta0VevMYLc18nMm9j69g3HC25mzz5Ly31GKhmU7faMEwTmDqeQlDE/xx3v8hRgjLL6SUS/06C2C0txmjzWdy3zTD2j5gblPD+OPIcp/YHsLoI6VCiEJgsL7ZS4y9J23HuOccwEjCn4j7hRCFQohyDAPyeSZeKfkP4PeBMiHE6+fiwOYDKeU3pJRbpJSbpZQflVImpZR3mI5MpJSOnG1/V0pZK6UcDLcOYqyA5RqZCSnlm6WU66WUHxsMCZVS/lJKuVVKuVFKeY+UMrZgBzkPSClbpZS7pJQbpJR/ahrSLWaoZO52Y15bwWhSyg+Zv/HbpZTR3L4EIKVcJ6XsMB9/zTxvm6SU/2y+9mkp5RrztT9YpOMADI+GhcV43Iph6CQwbrjfB/5HCPGbGLWLPjTJ5/8LQ+nl48BBxnpqlx1SyhaM2imDz9+Q8/hHwI/Mp2OWcM0bwlvG+d5fMixTPMjz5t+yQ0p5Sggx6PEB+BLD3keBIQP6l6M+0yeE+DDwYzNB+BSGwW2xgphm3/hb4ItCiF/ByG2ZCoMT/PeN8/53zfevn9kRzB3TGE/yXQebzO1eYDiEq5mx4whSyt9lpGz1uBPbPJ/NNzYtKFLKF4UQP8QwzDox7icFwO8x9p70S4wk9Dbga5N89XkMI6cW+Bcp5TkAIcSY/iiEeAtQJqX8mRCiB/imEGL3qHO4rBGGcMXvY4RSWlisOAZlKy0sLCwsLFY8pgf/BWkkv19xmBPbF4A/klK+ar52B4Z887LIYxBC/CXG/OXvFrstKwUhRC2Go+krUso/y3n9DpZR37CYO0zRmadX0qqWtdJjYWFhYXEl8X8wlNCuOEZNbF9d7PZMFWFImG+WUh4VhoT5hzAURS3mCCllN3nEQKSUz7NMIw4sZsfoleiVgLXSY2FhYWFxRSCEeA0IY9SvSS9ycyymiBCiFCMXrBJDVe4zUsovL26rLCwslhuW0WNhYWFhYWFhYWFhsaKx1NssLCwsLCwsLCwsLFY0ltFjYWFhYWFhYWFhYbGisYweCwsLCwsLCwsLC4sVjWX0WFhYWFhYWFhYWFisaCyjx8LCwsLCwsLCwsJiRfP/AQuGtIfjHnCeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x1008 with 121 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "attributes = ['brand', 'serial', 'model','color', 'cityId', 'country', 'maketype', 'gearbox', 'oiltype', 'af1', 'price']\n",
    "scatter_matrix(pd.DataFrame(std_scl.fit_transform(x_dp[attributes]), columns=x_dp[attributes].columns), figsize=(14, 14))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49cbbc9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T08:21:46.453512Z",
     "start_time": "2022-01-08T08:21:46.229965Z"
    }
   },
   "source": [
    "直接看最后一行散点图，发现存在某些难以描述的关系，考虑color，country，oiltype 三种类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c7acc84e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:30.334049Z",
     "start_time": "2022-01-12T14:57:30.255259Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price            1.000000\n",
       "newprice         0.797280\n",
       "displacement     0.574649\n",
       "af2              0.525054\n",
       "af8              0.487720\n",
       "maketype         0.427621\n",
       "modelyear        0.262118\n",
       "af5              0.245364\n",
       "serial           0.171404\n",
       "gearbox          0.138910\n",
       "brand            0.134091\n",
       "model            0.096874\n",
       "transferCount    0.080933\n",
       "seatings         0.045485\n",
       "af6              0.036240\n",
       "carid            0.014786\n",
       "oiltype          0.013378\n",
       "af1              0.005986\n",
       "af3             -0.014383\n",
       "af9             -0.021742\n",
       "af14            -0.025182\n",
       "tradeTime       -0.036786\n",
       "af10            -0.038331\n",
       "color           -0.059754\n",
       "carCode         -0.065444\n",
       "mileage         -0.128726\n",
       "cityId          -0.130504\n",
       "registerDate    -0.244779\n",
       "af13            -0.246824\n",
       "country         -0.248621\n",
       "lincenseDate    -0.271013\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = x_dp.corr()\n",
    "corr_matrix['price'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e21e8453",
   "metadata": {},
   "source": [
    "结合散点图和相关系数表，我们考虑删除以下分类变量（依据是相关系数绝对值小于0.2，或者散点图大致为均匀分布的）：\n",
    "\n",
    "'brand', 'serial', 'model', 'color', 'cityId', 'gearbox', 'oiltype', 'af1'\n",
    "\n",
    "仅保留了'country'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c3dbc5f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:30.350006Z",
     "start_time": "2022-01-12T14:57:30.335090Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp.drop(['brand', 'serial', 'model', 'color', 'cityId', 'gearbox', 'oiltype', 'af1'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f3178426",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:30.381922Z",
     "start_time": "2022-01-12T14:57:30.351004Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carid</th>\n",
       "      <th>tradeTime</th>\n",
       "      <th>mileage</th>\n",
       "      <th>carCode</th>\n",
       "      <th>transferCount</th>\n",
       "      <th>seatings</th>\n",
       "      <th>registerDate</th>\n",
       "      <th>lincenseDate</th>\n",
       "      <th>country</th>\n",
       "      <th>maketype</th>\n",
       "      <th>...</th>\n",
       "      <th>af5</th>\n",
       "      <th>af6</th>\n",
       "      <th>af8</th>\n",
       "      <th>af9</th>\n",
       "      <th>af10</th>\n",
       "      <th>af11</th>\n",
       "      <th>af12</th>\n",
       "      <th>af13</th>\n",
       "      <th>af14</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>4.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1217</td>\n",
       "      <td>1256</td>\n",
       "      <td>779413.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>4220*1740*1625</td>\n",
       "      <td>301</td>\n",
       "      <td>1</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>8.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1582</td>\n",
       "      <td>1567</td>\n",
       "      <td>779415.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4630*1775*1480</td>\n",
       "      <td>401</td>\n",
       "      <td>2</td>\n",
       "      <td>7.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>6.04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1704</td>\n",
       "      <td>1760</td>\n",
       "      <td>779413.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>4500*1834*1707</td>\n",
       "      <td>402</td>\n",
       "      <td>2</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>5.70</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3165</td>\n",
       "      <td>3233</td>\n",
       "      <td>779415.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>4315*1783*1606</td>\n",
       "      <td>806</td>\n",
       "      <td>1</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>882</td>\n",
       "      <td>923</td>\n",
       "      <td>779413.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.507934</td>\n",
       "      <td>1</td>\n",
       "      <td>4680*1785*1460</td>\n",
       "      <td>202</td>\n",
       "      <td>1</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29993</th>\n",
       "      <td>72533</td>\n",
       "      <td>177</td>\n",
       "      <td>6.87</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2708</td>\n",
       "      <td>2770</td>\n",
       "      <td>779419.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4725*1880*1686</td>\n",
       "      <td>798</td>\n",
       "      <td>1</td>\n",
       "      <td>10.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29994</th>\n",
       "      <td>27237</td>\n",
       "      <td>103</td>\n",
       "      <td>5.79</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1857</td>\n",
       "      <td>1830</td>\n",
       "      <td>779416.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4649*1859*1695</td>\n",
       "      <td>499</td>\n",
       "      <td>1</td>\n",
       "      <td>11.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29996</th>\n",
       "      <td>4294</td>\n",
       "      <td>423</td>\n",
       "      <td>11.69</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3318</td>\n",
       "      <td>3344</td>\n",
       "      <td>779415.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1+2</td>\n",
       "      <td>4960*1845*1480</td>\n",
       "      <td>809</td>\n",
       "      <td>2</td>\n",
       "      <td>8.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29997</th>\n",
       "      <td>20584</td>\n",
       "      <td>307</td>\n",
       "      <td>9.87</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3409</td>\n",
       "      <td>3440</td>\n",
       "      <td>779415.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>4665*1700*1510</td>\n",
       "      <td>1106</td>\n",
       "      <td>2</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29998</th>\n",
       "      <td>11490</td>\n",
       "      <td>264</td>\n",
       "      <td>9.57</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1826</td>\n",
       "      <td>1875</td>\n",
       "      <td>779415.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3+2</td>\n",
       "      <td>4915*1820*1450</td>\n",
       "      <td>502</td>\n",
       "      <td>1</td>\n",
       "      <td>28.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23969 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carid  tradeTime  mileage  carCode  transferCount  seatings  \\\n",
       "0          1         33     4.01      1.0              0         5   \n",
       "1          2         36     8.60      1.0              0         5   \n",
       "3          6         32     6.04      1.0              3         5   \n",
       "4          7         31     5.70      2.0              2         5   \n",
       "7         13         14     4.64      1.0              0         5   \n",
       "...      ...        ...      ...      ...            ...       ...   \n",
       "29993  72533        177     6.87      2.0              1         5   \n",
       "29994  27237        103     5.79      1.0              0         5   \n",
       "29996   4294        423    11.69      2.0              2         5   \n",
       "29997  20584        307     9.87      2.0              0         5   \n",
       "29998  11490        264     9.57      1.0              1         5   \n",
       "\n",
       "       registerDate  lincenseDate   country  maketype  ...  af5  af6  af8  \\\n",
       "0              1217          1256  779413.0       1.0  ...    1    1  1.0   \n",
       "1              1582          1567  779415.0       2.0  ...    2    2  2.0   \n",
       "3              1704          1760  779413.0       1.0  ...    6    1  2.0   \n",
       "4              3165          3233  779415.0       2.0  ...    7    1  1.0   \n",
       "7               882           923  779413.0       1.0  ...    6    2  2.0   \n",
       "...             ...           ...       ...       ...  ...  ...  ...  ...   \n",
       "29993          2708          2770  779419.0       2.0  ...   25    1  1.0   \n",
       "29994          1857          1830  779416.0       2.0  ...   46    1  1.0   \n",
       "29996          3318          3344  779415.0       2.0  ...   16    2  1.0   \n",
       "29997          3409          3440  779415.0       2.0  ...    7    2  1.0   \n",
       "29998          1826          1875  779415.0       3.0  ...   47    2  1.0   \n",
       "\n",
       "       af9      af10  af11            af12  af13  af14  price  \n",
       "0      5.0  2.000000     1  4220*1740*1625   301     1   4.24  \n",
       "1      4.0  3.000000   1+2  4630*1775*1480   401     2   7.38  \n",
       "3      5.0  2.000000     1  4500*1834*1707   402     2   4.38  \n",
       "4      5.0  2.000000     1  4315*1783*1606   806     1   5.90  \n",
       "7      4.0  2.507934     1  4680*1785*1460   202     1   5.95  \n",
       "...    ...       ...   ...             ...   ...   ...    ...  \n",
       "29993  5.0  2.000000   1+2  4725*1880*1686   798     1  10.40  \n",
       "29994  5.0  2.000000   1+2  4649*1859*1695   499     1  11.80  \n",
       "29996  4.0  3.000000   1+2  4960*1845*1480   809     2   8.30  \n",
       "29997  4.0  3.000000     1  4665*1700*1510  1106     2   5.00  \n",
       "29998  4.0  3.000000   3+2  4915*1820*1450   502     1  28.00  \n",
       "\n",
       "[23969 rows x 25 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077f6216",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T08:41:55.804112Z",
     "start_time": "2022-01-08T08:41:55.788156Z"
    }
   },
   "source": [
    "### 转化af12（乘出来变体积）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2527abc5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:30.556772Z",
     "start_time": "2022-01-12T14:57:30.382918Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp['af12'] = x_dp['af12'].apply(eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "13ac2a21",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:30.572759Z",
     "start_time": "2022-01-12T14:57:30.558450Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        11932050000\n",
       "1        12163010000\n",
       "3        14087871000\n",
       "4        12355993870\n",
       "7        12196548000\n",
       "            ...     \n",
       "29993    14976738000\n",
       "29994    14649022245\n",
       "29996    13543776000\n",
       "29997    11975055000\n",
       "29998    12970685000\n",
       "Name: af12, Length: 23969, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dp['af12']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "82e9569b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:37.764399Z",
     "start_time": "2022-01-12T14:57:30.574410Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp.to_excel(r'C:\\Users\\hasee\\OneDrive\\桌面\\2021年MathorCup大数据竞赛赛道A\\附件\\v4.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7cbc32",
   "metadata": {},
   "source": [
    "## 标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4d575449",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:44.307833Z",
     "start_time": "2022-01-12T14:57:37.766001Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp = pd.read_excel(r'C:\\Users\\hasee\\OneDrive\\桌面\\2021年MathorCup大数据竞赛赛道A\\附件\\v4.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "64cb8499",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:46.257308Z",
     "start_time": "2022-01-12T14:57:44.309516Z"
    }
   },
   "outputs": [],
   "source": [
    "x_t = pd.read_excel(r'C:\\Users\\hasee\\OneDrive\\桌面\\2021年MathorCup大数据竞赛赛道A\\附件\\附件2：估价验证数据.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "af450c9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:46.273267Z",
     "start_time": "2022-01-12T14:57:46.259303Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1+2        15549\n",
       "1           5075\n",
       "3+2         2728\n",
       "1+2,4+2      616\n",
       "5              1\n",
       "Name: af11, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dp.af11.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9dfa2be5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:46.288228Z",
     "start_time": "2022-01-12T14:57:46.275270Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1+2        3057\n",
       "1          1143\n",
       "3+2         571\n",
       "1+2,4+2     156\n",
       "Name: af11, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_t.af11.value_counts()  # 可见需要验证的数据里面没有“5”，因此从训练集删掉这一类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "812f512d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:46.304200Z",
     "start_time": "2022-01-12T14:57:46.290231Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp.drop(x_dp[x_dp['af11']==5].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d44cfbd3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:46.320183Z",
     "start_time": "2022-01-12T14:57:46.305182Z"
    }
   },
   "outputs": [],
   "source": [
    "x_dp.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "922eca35",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T08:48:07.237989Z",
     "start_time": "2022-01-08T08:48:07.227018Z"
    }
   },
   "source": [
    "### af11，country转化为独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6fd0fc64",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:46.384012Z",
     "start_time": "2022-01-12T14:57:46.322138Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ohe = OneHotEncoder()\n",
    "x_num = x_dp.drop(['country', 'af11', 'price'], axis=1)\n",
    "x_num_1 = pd.DataFrame(std_scl.fit_transform(x_num), columns=x_num.columns)\n",
    "x_2 = pd.concat([x_num_1, pd.DataFrame(ohe.fit_transform(x_dp[['country']]).toarray())], axis=1)\n",
    "x_2 = pd.concat([x_2, pd.DataFrame(ohe.fit_transform(x_dp[['af11']].astype(str)).toarray())], axis=1)\n",
    "x_2['price'] = x_dp['price']\n",
    "x_2.drop(['carid'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "975f3a9d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:56.454359Z",
     "start_time": "2022-01-12T14:57:46.384970Z"
    }
   },
   "outputs": [],
   "source": [
    "x_2.to_excel(r'C:\\Users\\hasee\\OneDrive\\桌面\\2021年MathorCup大数据竞赛赛道A\\附件\\v5.xlsx', index=False)  \n",
    "# 训练集处理完毕"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa5a3be",
   "metadata": {},
   "source": [
    "# 数据处理实例（弃用）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08ae4421",
   "metadata": {},
   "source": [
    "用一个类整合前面全部数据处理过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d480d795",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:56.485284Z",
     "start_time": "2022-01-12T14:57:56.456058Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler  # 标准化函数\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "import pandas as pd\n",
    "\n",
    "class wholl_process(BaseEstimator, TransformerMixin):\n",
    "        def __init__(self):\n",
    "            return None\n",
    "        def fit(self, X, y=None):\n",
    "            return self\n",
    "        def transform(self, X):\n",
    "            X.drop(['af15', 'af7', 'af4'], axis=1, inplace=True)\n",
    "            X['af10'] = X['af10'].fillna(X['af10'].mean())\n",
    "            X['af8'] = X['af8'].fillna(X['af8'].mean())\n",
    "            X.dropna(subset=['country'], how='all', inplace=True)\n",
    "            X.drop(X[X['country'] == 0].index, inplace=True)  # 不再保留包含0值的数据\n",
    "            X.dropna(subset=['af12', 'af13', 'af11', 'modelyear', 'af9', 'carCode'], axis=0, inplace=True)\n",
    "            X['af1'] = X['af1'].fillna(1)\n",
    "            X['af13'] = (X['af13'][0:].max() - \n",
    "                         X['af13'][0:]).astype('int32')\n",
    "            X['tradeTime'] = (X['tradeTime'][0:].max() - \n",
    "                                 X['tradeTime'][0:]).astype('str').apply(lambda x:x[:-5]).astype('int32')\n",
    "            X['registerDate'] = (X['registerDate'][0:].max() - \n",
    "                                 X['registerDate'][0:]).astype('str').apply(lambda x:x[:-5]).astype('int32')\n",
    "            X['lincenseDate'] = (X['lincenseDate'][0:].max() - \n",
    "                                 X['lincenseDate'][0:]).astype('str').apply(lambda x:x[:-5]).astype('int32')\n",
    "            \n",
    "            X.drop(['brand', 'serial', 'model', 'color', 'cityId', 'gearbox', 'oiltype', 'af1'], axis =1, inplace=True)\n",
    "            X['af12'] = X['af12'].astype(str).apply(eval)\n",
    "            \n",
    "            std_scl = StandardScaler()\n",
    "            ohe = OneHotEncoder()\n",
    "            \n",
    "            X_num = X.drop(['country', 'af11'], axis=1)\n",
    "            X_num_1 = pd.DataFrame(std_scl.fit_transform(X_num), columns=X_num.columns)\n",
    "            X_2 = pd.concat([X_num_1, pd.DataFrame(ohe.fit_transform(X[['country']]).toarray())], axis=1)\n",
    "            X = pd.concat([X_2, pd.DataFrame(ohe.fit_transform(X[['af11']].astype(str)).toarray())], axis=1)\n",
    "            X.drop(['carid'], axis=1, inplace=True)\n",
    "            return X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d20c936",
   "metadata": {},
   "source": [
    "# 机器学习"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c42378",
   "metadata": {},
   "source": [
    "## 划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "50713fe2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:56.737305Z",
     "start_time": "2022-01-12T14:57:56.691428Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pf\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set, test_set = train_test_split(x_2, test_size=0.2, random_state=42)  # 数据分割为训练集和测试集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "753ef33a",
   "metadata": {},
   "source": [
    "## 线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ad3fedf7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:56.753303Z",
     "start_time": "2022-01-12T14:57:56.738302Z"
    }
   },
   "outputs": [],
   "source": [
    "x_train = train_set.drop(['price'], axis=1)\n",
    "x_train_labels = train_set['price']\n",
    "x_test = test_set.drop(['price'], axis=1)\n",
    "x_test_labels = test_set['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "37b3af09",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:56.817091Z",
     "start_time": "2022-01-12T14:57:56.754261Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "line_model = LinearRegression()\n",
    "line_model.fit(x_train, x_train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19ec9aeb",
   "metadata": {},
   "source": [
    "### 简单评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "07fde374",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:56.832051Z",
     "start_time": "2022-01-12T14:57:56.819086Z"
    }
   },
   "outputs": [],
   "source": [
    "some_data = x_train[:5]\n",
    "some_labels = x_train_labels[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "23e6b598",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:56.848008Z",
     "start_time": "2022-01-12T14:57:56.833072Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测： [35.94567773 15.61743598  6.75662441 17.65294924  9.34030762]\n",
      "实际： [50.8  11.    7.88 18.    6.48]\n"
     ]
    }
   ],
   "source": [
    "print('预测：',line_model.predict(some_data))\n",
    "print('实际：', some_labels.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9ae40258",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:56.878927Z",
     "start_time": "2022-01-12T14:57:56.850004Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "标准差： 5.863933946275425\n",
      "R2： 0.7946078553812701\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    19174.000000\n",
       "mean        14.022629\n",
       "std         12.939231\n",
       "min          0.050000\n",
       "25%          6.380000\n",
       "50%         10.500000\n",
       "75%         17.880000\n",
       "max        276.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score  # 计算均方差\n",
    "\n",
    "price_predictions = line_model.predict(x_train)\n",
    "lin_mse = mean_squared_error(x_train_labels, price_predictions)\n",
    "print('标准差：', np.sqrt(lin_mse))\n",
    "print('R2：', r2_score(x_train_labels, price_predictions))\n",
    "x_train_labels.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "96870d43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:57.134296Z",
     "start_time": "2022-01-12T14:57:56.879925Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(x_train_labels, bins=100, edgecolor = 'k')  # 价格（标准化后）的分布图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3f8e3696",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:57.353659Z",
     "start_time": "2022-01-12T14:57:57.136241Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c091b93850>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(x_train_labels-price_predictions)), list((x_train_labels-price_predictions)))  # 残差图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32bffaeb",
   "metadata": {},
   "source": [
    "残差基本正常"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82ed03cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T14:38:59.955214Z",
     "start_time": "2022-01-08T14:38:59.947238Z"
    }
   },
   "source": [
    "### K折交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "fa3c1806",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:57.545145Z",
     "start_time": "2022-01-12T14:57:57.360675Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(line_model, x_train, x_train_labels, scoring='neg_mean_squared_error', cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "63b78747",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:57.561103Z",
     "start_time": "2022-01-12T14:57:57.546144Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-31.63051983, -33.32850794, -30.6035274 , -47.79834271,\n",
       "       -42.98286044, -31.74441968, -25.68661612, -38.63198212,\n",
       "       -34.55248281, -29.71893624])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "70ed6113",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:57.577060Z",
     "start_time": "2022-01-12T14:57:57.563098Z"
    }
   },
   "outputs": [],
   "source": [
    "lin_rmse_socres = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b0238d74",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:57.593018Z",
     "start_time": "2022-01-12T14:57:57.578059Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.62410169, 5.77308479, 5.5320455 , 6.91363455, 6.55613151,\n",
       "       5.63421864, 5.06819654, 6.21546315, 5.878136  , 5.4515077 ])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_rmse_socres  # 标准差比较稳定"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7de9ee01",
   "metadata": {},
   "source": [
    "### 在验证集评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "55c15afe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:57:57.608976Z",
     "start_time": "2022-01-12T14:57:57.594016Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.6043864273151955\n"
     ]
    }
   ],
   "source": [
    "x_lin_predict = line_model.predict(x_test)\n",
    "line_predict_mse = mean_squared_error(x_test_labels, x_lin_predict)\n",
    "print(np.sqrt(line_predict_mse))  # 输出标准差"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a51655",
   "metadata": {},
   "source": [
    "## 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a88b1839",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:58:10.647136Z",
     "start_time": "2022-01-12T14:57:57.609973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(x_train, x_train_labels)  # 训练有素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ef0c6953",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:58:10.679051Z",
     "start_time": "2022-01-12T14:58:10.648133Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测： [55.2498   11.4949    7.9264   18.404997  6.8796  ]\n",
      "实际： [50.8  11.    7.88 18.    6.48]\n"
     ]
    }
   ],
   "source": [
    "print('预测：', forest_reg.predict(some_data))\n",
    "print('实际：', some_labels.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3130c7f",
   "metadata": {},
   "source": [
    "### 简单评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "739c53ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:58:11.155087Z",
     "start_time": "2022-01-12T14:58:10.681045Z"
    }
   },
   "outputs": [],
   "source": [
    "price_predictions = forest_reg.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8fd44e41",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:58:11.378183Z",
     "start_time": "2022-01-12T14:58:11.155777Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c094b365e0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(x_train_labels-price_predictions)), list((x_train_labels-price_predictions)))  # 残差图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533137aa",
   "metadata": {},
   "source": [
    "存在一些异常值，大体稳定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4510a11f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:58:11.839988Z",
     "start_time": "2022-01-12T14:58:11.379180Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.076616892892414\n"
     ]
    }
   ],
   "source": [
    "forest_predictions = forest_reg.predict(x_train)\n",
    "forest_mse = mean_squared_error(x_train_labels, forest_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "print(forest_rmse)  # 训练集的标准差"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d82a953",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T14:50:13.807619Z",
     "start_time": "2022-01-08T14:50:13.798644Z"
    }
   },
   "source": [
    "### K折交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "6d3d6573",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:58:11.855906Z",
     "start_time": "2022-01-12T14:58:11.840947Z"
    }
   },
   "outputs": [],
   "source": [
    "# 编写一个函数用于输出结果\n",
    "def display_scores(scores):\n",
    "    print('标准差情况:', scores)\n",
    "    print('均值:', scores.mean())\n",
    "    print('（标准差的）标准差:', scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4376b4e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:58:58.750594Z",
     "start_time": "2022-01-12T14:58:11.856903Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "标准差情况: [2.70298827 2.90427364 3.0150235  3.14265405 2.4942151 ]\n",
      "均值: 2.851830914145989\n",
      "（标准差的）标准差: 0.22978396866111525\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(forest_reg, x_train, x_train_labels, scoring='neg_mean_squared_error', cv=5)\n",
    "forest_rmse_socres = np.sqrt(-scores)\n",
    "display_scores(forest_rmse_socres)  # 查看得分情况"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c432d746",
   "metadata": {},
   "source": [
    "### 验证集评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fc78cd83",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:58:58.892216Z",
     "start_time": "2022-01-12T14:58:58.752589Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2316216777930102\n"
     ]
    }
   ],
   "source": [
    "x_test_forest_predict = forest_reg.predict(x_test)\n",
    "line_predict_mse = mean_squared_error(x_test_labels, x_test_forest_predict)\n",
    "print(np.sqrt(line_predict_mse))  # 输出标准差"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91304e12",
   "metadata": {},
   "source": [
    "比线性回归误差小"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89230f4e",
   "metadata": {},
   "source": [
    "# 精度评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "788d3474",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:58:58.908174Z",
     "start_time": "2022-01-12T14:58:58.893214Z"
    }
   },
   "outputs": [],
   "source": [
    "def Accuracy(y_true, y_pred):\n",
    "    n = len(y_true)\n",
    "    mape = sum(np.abs((y_true - y_pred)/y_true))/n\n",
    "    Apexi = pd.DataFrame(abs(y_true - y_pred)/y_true)\n",
    "    Accuracy = (Apexi[Apexi <= 0.5].count() /Apexi.count())*0.8+0.2*(1-mape)\n",
    "    return Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "76455ff2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:58:58.924166Z",
     "start_time": "2022-01-12T14:58:58.909170Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price    0.974913\n",
       "dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Accuracy(x_test_labels, x_test_forest_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0c375c9b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T14:58:58.940086Z",
     "start_time": "2022-01-12T14:58:58.926125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price    0.784039\n",
       "dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Accuracy(x_test_labels, x_lin_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f5233e",
   "metadata": {},
   "source": [
    "随机森林在测试集精度97%  \n",
    "线性回归精度78%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "3e73417e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T15:18:19.615996Z",
     "start_time": "2022-01-12T15:18:19.356751Z"
    }
   },
   "outputs": [],
   "source": [
    "import joblib\n",
    "joblib.dump(forest_reg, 'forest_model.pkl')  # 保存模型（在程序同一目录）\n",
    "forest_reg = joblib.load('forest_model.pkl')  # 读取模型\n",
    "\n",
    "# forest_reg.predict( 预测数据 ) 即可得到预测值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95bcfc65",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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